In this file, we presented the results from analyses of exposures against computed epigenetic age accelerations (EAA) calculated using DNA methylation data by different methods. For each exposure, we conducted both primary analyses (likelihood ratio tests and generalized estimating equations (GEE), adjusted for confounders) and sensitivity analyses (likelihood ratio tests and/or generalized estimating equations (GEE) limited to certain fuel users, not adjusted for confounders). In addition to what’s included in the analysis plan, we also analyzed ambient and urinary exposures.

1. Description of study population

There are 129 visits with corresponding epigenetic ages available among 106 female subjects. For these 106 subjects, 83 have been visited once and 23 have been visited twice.

The following tables summarize all the information of the first visit of these 106 subjects.

Baseline characteristics (confounders)

Characteristic N = 1061
Age 56 (15)
county
Fuyuan 51 / 96 (53%)
Xuanwe 45 / 96 (47%)
(Missing) 10
BMI 22.0 (3.5)
ses
0 47 / 96 (49%)
1 49 / 96 (51%)
(Missing) 10
edu
1 63 / 96 (66%)
2 16 / 96 (17%)
3 13 / 96 (14%)
4 4 / 96 (4.2%)
(Missing) 10
1 Mean (SD); n / N (%)

Epigenetic ages

Characteristic N = 1061
DNAmAge 56 (14)
DNAmAgeHannum 59 (14)
DNAmPhenoAge 55 (14)
DNAmAgeSkinBloodClock 56 (13)
DNAmGrimAge 55 (12)
DNAmTL 6.84 (0.33)
1 Mean (SD)

Epigenetic ages accelarations

Characteristic N = 1061
AgeAccelerationResidual 0.2 (4.7)
AgeAccelerationResidualHannum -0.5 (4.1)
AgeAccelPheno -0.7 (4.5)
DNAmAgeSkinBloodClockAdjAge 0.0 (3.4)
AgeAccelGrim -0.31 (3.02)
DNAmTLAdjAge 0.03 (0.18)
IEAA 0.1 (4.4)
EEAA -0.6 (5.3)
1 Mean (SD)

Fuel/stove type exposures

Characteristic N = 1061
curFuel
Smokeles 12 / 90 (13%)
Smoky 72 / 90 (80%)
Wood_and_or_Plant 6 / 90 (6.7%)
(Missing) 16
brthFuel
Mix 42 / 93 (45%)
Smokeles 3 / 93 (3.2%)
Smoky 40 / 93 (43%)
Wood 8 / 93 (8.6%)
(Missing) 13
cumFuel
Mix 64 / 96 (67%)
Smoky 32 / 96 (33%)
(Missing) 10
curStove
Firepit_and_unventilated 16 / 90 (18%)
Mix 14 / 90 (16%)
Portable_stove 16 / 90 (18%)
Ventilated 44 / 90 (49%)
(Missing) 16
1 n / N (%)

5MC exposures

Characteristic N = 1061
cur_5mc 8.2 (4.2)
(Missing) 12
cum_5mc 269 (152)
(Missing) 12
bir_5mc 5.23 (2.83)
(Missing) 12
cur_5mc_measured 14 (42)
(Missing) 65
1 Mean (SD)
## [1] "Pearson pair-wise correlation:"
##                    cur_5mc   cum_5mc     bir_5mc cur_5mc_measured
## cur_5mc          1.0000000 0.7055284  0.70960439       0.10311689
## cum_5mc          0.7055284 1.0000000  0.84246687       0.17631136
## bir_5mc          0.7096044 0.8424669  1.00000000      -0.04507814
## cur_5mc_measured 0.1031169 0.1763114 -0.04507814       1.00000000
## [1] "Spearman pair-wise correlation:"
##                    cur_5mc   cum_5mc   bir_5mc cur_5mc_measured
## cur_5mc          1.0000000 0.6580617 0.6721831        0.4434641
## cum_5mc          0.6580617 1.0000000 0.8313456        0.3335835
## bir_5mc          0.6721831 0.8313456 1.0000000        0.2297288
## cur_5mc_measured 0.4434641 0.3335835 0.2297288        1.0000000

Cluster-based exposures

clusCUR6

Clusters based on model-based exposure estimates at or shortly before the visit

Characteristic N = 1061
CUR6_BC_PAH6 0.25 (0.95)
(Missing) 12
CUR6_PAH31 0.21 (0.89)
(Missing) 12
CUR6_NkF -0.08 (1.08)
(Missing) 12
CUR6_PM_RET 0.02 (0.93)
(Missing) 12
CUR6_NO2 0.15 (0.99)
(Missing) 12
CUR6_SO2 -0.18 (0.89)
(Missing) 12
1 Mean (SD)

clusCHLD5

Clusters based on model-based exposure estimates accrued before age 18

Characteristic N = 1061
CHLD5_X7 -0.05 (0.87)
(Missing) 12
CHLD5_X33 0.15 (0.97)
(Missing) 12
CHLD5_NkF -0.11 (1.13)
(Missing) 12
CHLD5_NO2 0.18 (1.12)
(Missing) 12
CHLD5_SO2 -0.04 (0.88)
(Missing) 12
1 Mean (SD)

clusCUM6

Clusters based on model-based lifetime exposure estimates

Characteristic N = 1061
CUM6_BC_NO2_PM 0.02 (1.06)
(Missing) 12
CUM6_PAH36 0.15 (0.96)
(Missing) 12
CUM6_DlP -0.23 (1.04)
(Missing) 12
CUM6_NkF -0.09 (1.14)
(Missing) 12
CUM6_RET -0.12 (0.97)
(Missing) 12
CUM6_SO2 -0.16 (0.92)
(Missing) 12
1 Mean (SD)

clusMEAS6

Clusters based on pollutant measurements

Characteristic N = 1061
MEAS6_BC_PM_RET 0.15 (0.89)
(Missing) 67
MEAS6_X31 0.25 (0.90)
(Missing) 67
MEAS6_X5 0.06 (0.98)
(Missing) 67
MEAS6_DlP 0.06 (1.03)
(Missing) 67
MEAS6_NkF 0.17 (1.06)
(Missing) 67
MEAS6_NO2_SO2 -0.12 (0.90)
(Missing) 67
1 Mean (SD)

clusURI5

Clusters based on urinary biomarkers

Characteristic N = 1061
URI5_NAP_1M_2M 0.01 (0.97)
(Missing) 13
URI5_ACE -0.12 (0.99)
(Missing) 13
URI5_FLU_PHE -0.04 (0.96)
(Missing) 13
URI5_PYR -0.06 (0.94)
(Missing) 13
URI5_CHR -0.02 (1.03)
(Missing) 13
1 Mean (SD)

Ambient exposures

Characteristic N = 1061
bap_air 66 (91)
(Missing) 3
pm25_air 205 (188)
ANY_air 908 (1,545)
(Missing) 33
BPE_air 69 (93)
(Missing) 3
BaA_air 91 (153)
(Missing) 3
BbF_air 110 (151)
(Missing) 3
BkF_air 24 (33)
(Missing) 3
CHR_air 88 (141)
(Missing) 3
DBA_air 23 (36)
(Missing) 3
FLT_air 65 (146)
(Missing) 3
FLU_air 441 (691)
(Missing) 33
IPY_air 41 (50)
(Missing) 3
NAP_air 5,342 (8,071)
(Missing) 33
PHE_air 675 (1,079)
(Missing) 33
PYR_air 71 (149)
(Missing) 3
1 Mean (SD)

Urinary biomarkers

Characteristic N = 1061
Benzanthracene_Chrysene_urine 0.98 (3.49)
(Missing) 2
Naphthalene_urine 247 (755)
Methylnaphthalene_2_urine 49 (65)
(Missing) 8
Methylnaphthalene_1_urine 21 (26)
(Missing) 3
Acenaphthene_urine 8 (11)
Phenanthrene_Anthracene_urine 216 (296)
Fluoranthene_urine 22 (25)
Pyrene_urine 0.74 (0.62)
(Missing) 15
1 Mean (SD)

2.1. Current (self-reported) fuel type

The numbers of observations with each current fuel type:

## 
##          Smokeles             Smoky Wood_and_or_Plant 
##                17                87                 8

Primary analysis

Investigate the association with current (self-reported) fuel type in the LEX study participants, adjusting for known confounders. The reference group for this analysis would be the smoky coal users. This would be a categorical analysis, and the results would be a p-value from the likelihood ratio (LR) test of a confounder-only model to a model including the exposure variables, as well as p-values for the contrast of each category of coal use (smokeless coal or plant/wood) to that of smoky coal. FDR correction should be used separately for each of these sets. The main interest would be in the coal-specific findings and perhaps less so in the results from the LR test.

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1398       0.5531
## Hannum EAA     0.4995       0.5531
## PhenoAge EAA   0.4880       0.5531
## Skin&Blood EAA 0.4608       0.5531
## GrimAge EAA    0.0306       0.2448
## DNAmTL         0.4376       0.5531
## IEAA           0.2887       0.5531
## EEAA           0.5531       0.5531

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between current fuel type and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of current fuel type, smokeless/wood or plant compared to smoky coal:
##                             coefficient    std ci_lower ci_upper  p_val
## Smoky (reference/intercept)      4.5873 1.9055   0.8525   8.3222 0.0161
## Smokeles                        -1.7864 0.7216  -3.2008  -0.3719 0.0133
## Wood_and_or_Plant                0.6040 1.5782  -2.4893   3.6972 0.7019
##                             sig_level
## Smoky (reference/intercept)   <= 0.05
## Smokeles                      <= 0.05
## Wood_and_or_Plant              > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2800       0.8200
## Hannum EAA     0.4890       0.8819
## PhenoAge EAA   0.8936       0.8936
## Skin&Blood EAA 0.5512       0.8819
## GrimAge EAA    0.1672       0.8200
## DNAmTL         0.8624       0.8936
## IEAA           0.3075       0.8200
## EEAA           0.6635       0.8847

GEE (no confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between current fuel type and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of current fuel type, smokeless/wood or plant compared to smoky coal:
##                             coefficient    std ci_lower ci_upper  p_val
## Smoky (reference/intercept)     -0.3652 0.3139  -0.9805   0.2501 0.2447
## Smokeles                        -1.1498 0.5719  -2.2707  -0.0289 0.0444
## Wood_and_or_Plant                0.4092 1.4927  -2.5166   3.3349 0.7840
##                             sig_level
## Smoky (reference/intercept)    > 0.05
## Smokeles                      <= 0.05
## Wood_and_or_Plant              > 0.05

2.2. Cumulative lifetime (self-reported) fuel type

The numbers of observations with each cumulative lifetime fuel type:

## 
##   Mix Smoky 
##    82    37

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1656       0.4416
## Hannum EAA     0.8619       0.9201
## PhenoAge EAA   0.5503       0.8805
## Skin&Blood EAA 0.9201       0.9201
## GrimAge EAA    0.0676       0.2704
## DNAmTL         0.4802       0.8805
## IEAA           0.0532       0.2704
## EEAA           0.8262       0.9201

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between cumulative fuel type and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of cumulative fuel type, mix compared to smoky coal:
##                             coefficient    std ci_lower ci_upper  p_val
## Smoky (reference/intercept)      4.2542 1.7709   0.7832   7.7252 0.0163
## Mix                             -1.0164 0.5314  -2.0578   0.0251 0.0558
##                             sig_level
## Smoky (reference/intercept)   <= 0.05
## Mix                            > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Mix}) + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3532       0.8414
## Hannum EAA     0.7909       0.8414
## PhenoAge EAA   0.8253       0.8414
## Skin&Blood EAA 0.8414       0.8414
## GrimAge EAA    0.1805       0.7220
## DNAmTL         0.6405       0.8414
## IEAA           0.0759       0.6072
## EEAA           0.6484       0.8414

GEE (no confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between cumulative fuel type and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of cumulative fuel type, mix compared to smoky coal:
##                             coefficient    std ci_lower ci_upper  p_val
## Smoky (reference/intercept)      0.0275 0.4057  -0.7676   0.8227 0.9459
## Mix                             -0.7259 0.5543  -1.8124   0.3606 0.1904
##                             sig_level
## Smoky (reference/intercept)    > 0.05
## Mix                            > 0.05

2.3. Childhood (self-reported) fuel type

The numbers of observations with each current fuel type:

## 
##      Mix Smokeles    Smoky     Wood 
##       53        5       47       11

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1845       0.4920
## Hannum EAA     0.3512       0.5619
## PhenoAge EAA   0.7387       0.7387
## Skin&Blood EAA 0.7259       0.7387
## GrimAge EAA    0.0116       0.0928
## DNAmTL         0.5352       0.7136
## IEAA           0.0811       0.3244
## EEAA           0.3171       0.5619

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the association between current fuel type and the Grim Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of childhood fuel type, smokeless/wood/mixed compared to smoky coal:
##                             coefficient    std ci_lower ci_upper  p_val
## Smoky (reference/intercept)      4.0266 1.7472   0.6022   7.4511 0.0212
## Wood                             0.1554 0.8731  -1.5559   1.8668 0.8587
## Smokeles                        -3.8872 1.1879  -6.2155  -1.5589 0.0011
## Mix                             -1.4618 0.5666  -2.5724  -0.3512 0.0099
##                             sig_level
## Smoky (reference/intercept)   <= 0.05
## Wood                           > 0.05
## Smokeles                      <= 0.01
## Mix                           <= 0.01

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2833       0.6101
## Hannum EAA     0.3813       0.6101
## PhenoAge EAA   0.8336       0.8336
## Skin&Blood EAA 0.7398       0.8336
## GrimAge EAA    0.0146       0.1168
## DNAmTL         0.5919       0.7892
## IEAA           0.1220       0.4880
## EEAA           0.3340       0.6101

GEE (No confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the association between current fuel type and the Grim Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of childhood fuel type, smokeless/wood/mixed compared to smoky coal:
##                             coefficient    std ci_lower ci_upper  p_val
## Smoky (reference/intercept)      0.3085 0.3954  -0.4665   1.0835 0.4353
## Wood                             0.1104 0.9417  -1.7353   1.9561 0.9067
## Smokeles                        -3.8546 1.3488  -6.4982  -1.2109 0.0043
## Mix                             -1.3960 0.6046  -2.5810  -0.2110 0.0209
##                             sig_level
## Smoky (reference/intercept)    > 0.05
## Wood                           > 0.05
## Smokeles                      <= 0.01
## Mix                           <= 0.05

3.1. Clusters based on model-based exposure estimates at or shortly before the visit (clusCUR6)

The file “LEX_clusCUR6.csv” has information on current pollutant exposures, obtained for the 2 years preceding the visit. To reduce multi-collinearity between exposures, exposure prototypes were derived based on hierarchical cluster analysis in combination followed by principal components analysis. These estimates are available for 6 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CUR6_BC_PAH6 – Black carbon (BC) and 6 PAHs
CUR6_PAH31 – a large cluster of 31 PAHs
CUR6_NkF – NkF only
CUR6_PM_RET – Particulate matter (PM) and retene
CUR6_NO2 – NO2 only
CUR6_SO2 – SO2 only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
CUR6_BC_PAH6 0.79 (-0.5, 0.8) -1.32 (-1.4, -0.9) 0.80 (-0.2, 1.1) 0.69 (0.1, 0.7)
(Missing) 3 2 1 0
CUR6_PAH31 0.38 (-0.4, 0.6) -1.14 (-1.4, -0.5) 0.46 (-0.1, 0.6) 0.75 (0.4, 0.8)
(Missing) 3 2 1 0
CUR6_NkF -0.40 (-0.6, 0.7) 0.06 (-0.2, 0.3) -0.51 (-0.6, 0.9) 0.74 (-0.2, 0.7)
(Missing) 3 2 1 0
CUR6_PM_RET -0.32 (-0.5, 0.4) -0.04 (-0.9, 0.3) -0.32 (-0.5, 0.1) 2.49 (0.9, 2.6)
(Missing) 3 2 1 0
CUR6_NO2 0.06 (-0.4, 0.8) 1.00 (0.6, 1.4) -0.06 (-0.5, 0.5) 0.63 (-0.2, 1.3)
(Missing) 3 2 1 0
CUR6_SO2 -0.30 (-0.9, 0.3) 1.37 (0.2, 1.5) -0.30 (-0.9, 0.1) -1.00 (-1.3, -0.9)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1718       0.3058
## Hannum EAA     0.2726       0.3635
## PhenoAge EAA   0.0174       0.0680
## Skin&Blood EAA 0.0255       0.0680
## GrimAge EAA    0.0028       0.0224
## DNAmTL         0.4552       0.4552
## IEAA           0.4430       0.4552
## EEAA           0.1911       0.3058

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $CUR6_BC_PAH6
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.6761 0.6262  -1.9035   0.5512 0.2803
## AgeAccelerationResidualHannum     -0.3906 0.4991  -1.3688   0.5877 0.4339
## AgeAccelPheno                     -0.1098 0.4162  -0.9256   0.7059 0.7918
## DNAmAgeSkinBloodClockAdjAge       -0.1170 0.4173  -0.9348   0.7008 0.7792
## AgeAccelGrim                       0.7687 0.2873   0.2056   1.3318 0.0075
## DNAmTLAdjAge                       0.0275 0.0235  -0.0186   0.0736 0.2423
## IEAA                              -0.2881 0.5474  -1.3611   0.7849 0.5987
## EEAA                              -0.7446 0.6235  -1.9667   0.4776 0.2324
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_PAH31
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1799 0.5996  -0.9953   1.3551 0.7641
## AgeAccelerationResidualHannum     -0.2175 0.5319  -1.2601   0.8250 0.6826
## AgeAccelPheno                      0.0564 0.4237  -0.7741   0.8868 0.8942
## DNAmAgeSkinBloodClockAdjAge        0.3281 0.4025  -0.4608   1.1170 0.4150
## AgeAccelGrim                       0.9568 0.2431   0.4803   1.4333 0.0001
## DNAmTLAdjAge                      -0.0096 0.0180  -0.0448   0.0257 0.5943
## IEAA                               0.1555 0.6228  -1.0652   1.3762 0.8029
## EEAA                              -0.3147 0.6558  -1.6000   0.9707 0.6313
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                   <= 0.001
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.1361 0.5107  -1.1371   0.8649 0.7899
## AgeAccelerationResidualHannum     -0.3542 0.4403  -1.2171   0.5087 0.4211
## AgeAccelPheno                     -0.5080 0.4155  -1.3224   0.3064 0.2215
## DNAmAgeSkinBloodClockAdjAge       -0.1739 0.3811  -0.9209   0.5732 0.6482
## AgeAccelGrim                       0.4613 0.2393  -0.0077   0.9304 0.0539
## DNAmTLAdjAge                      -0.0300 0.0190  -0.0673   0.0072 0.1138
## IEAA                              -0.1198 0.3900  -0.8842   0.6445 0.7586
## EEAA                              -0.3215 0.5837  -1.4655   0.8225 0.5817
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_PM_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.2829 0.5422  -0.7797   1.3456 0.6018
## AgeAccelerationResidualHannum     -0.5943 0.4604  -1.4967   0.3082 0.1968
## AgeAccelPheno                     -0.8820 0.4738  -1.8107   0.0468 0.0627
## DNAmAgeSkinBloodClockAdjAge       -0.5316 0.5129  -1.5370   0.4737 0.3000
## AgeAccelGrim                       0.7117 0.4002  -0.0727   1.4961 0.0754
## DNAmTLAdjAge                       0.0012 0.0248  -0.0474   0.0499 0.9605
## IEAA                               0.4740 0.4784  -0.4636   1.4116 0.3218
## EEAA                              -0.6387 0.6418  -1.8967   0.6193 0.3197
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_NO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.6950 0.5078  -0.3002   1.6903 0.1711
## AgeAccelerationResidualHannum     -0.0270 0.4125  -0.8355   0.7815 0.9479
## AgeAccelPheno                     -0.0108 0.4477  -0.8883   0.8667 0.9807
## DNAmAgeSkinBloodClockAdjAge        0.3085 0.3884  -0.4528   1.0698 0.4270
## AgeAccelGrim                      -0.0321 0.2850  -0.5907   0.5265 0.9104
## DNAmTLAdjAge                       0.0137 0.0180  -0.0216   0.0490 0.4457
## IEAA                               0.4456 0.4447  -0.4260   1.3172 0.3164
## EEAA                               0.0012 0.5778  -1.1313   1.1336 0.9984
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2870 0.5430  -1.3514   0.7774 0.5972
## AgeAccelerationResidualHannum     -0.4429 0.5619  -1.5442   0.6585 0.4306
## AgeAccelPheno                     -0.4050 0.5353  -1.4541   0.6442 0.4493
## DNAmAgeSkinBloodClockAdjAge       -0.4975 0.4795  -1.4373   0.4423 0.2995
## AgeAccelGrim                      -0.6569 0.3359  -1.3153   0.0014 0.0505
## DNAmTLAdjAge                       0.0021 0.0212  -0.0394   0.0437 0.9197
## IEAA                              -0.5388 0.4975  -1.5140   0.4364 0.2788
## EEAA                              -0.6422 0.6638  -1.9433   0.6588 0.3333
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

GEE (Mix, mutual adjust)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF + \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## The estimated effects:
## $CUR6_BC_PAH6
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -1.6422 1.0352  -3.6713   0.3868 0.1127
## AgeAccelerationResidualHannum     -1.1232 0.7420  -2.5776   0.3311 0.1301
## AgeAccelPheno                     -1.0110 0.6283  -2.2425   0.2204 0.1076
## DNAmAgeSkinBloodClockAdjAge       -0.9548 0.7991  -2.5211   0.6114 0.2321
## AgeAccelGrim                       0.6418 0.4580  -0.2559   1.5395 0.1611
## DNAmTLAdjAge                       0.0348 0.0327  -0.0293   0.0989 0.2871
## IEAA                              -0.9920 0.7357  -2.4341   0.4501 0.1776
## EEAA                              -1.7435 0.9797  -3.6637   0.1767 0.0751
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_PAH31
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            1.0694 0.9220  -0.7378   2.8766 0.2461
## AgeAccelerationResidualHannum      0.8481 0.8262  -0.7714   2.4675 0.3047
## AgeAccelPheno                      1.4674 0.6556   0.1825   2.7523 0.0252
## DNAmAgeSkinBloodClockAdjAge        1.4658 0.7189   0.0567   2.8749 0.0415
## AgeAccelGrim                       0.3424 0.4309  -0.5021   1.1869 0.4268
## DNAmTLAdjAge                      -0.0237 0.0288  -0.0800   0.0327 0.4109
## IEAA                               0.4782 0.8722  -1.2314   2.1877 0.5835
## EEAA                               1.0678 1.0463  -0.9830   3.1186 0.3075
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.8479 0.8031  -2.4221   0.7262 0.2911
## AgeAccelerationResidualHannum     -0.4910 0.6889  -1.8412   0.8593 0.4760
## AgeAccelPheno                     -0.6177 0.5169  -1.6308   0.3955 0.2321
## DNAmAgeSkinBloodClockAdjAge       -0.3074 0.6923  -1.6643   1.0494 0.6570
## AgeAccelGrim                       0.5426 0.3387  -0.1213   1.2065 0.1092
## DNAmTLAdjAge                      -0.0251 0.0225  -0.0693   0.0190 0.2648
## IEAA                              -0.5726 0.5374  -1.6259   0.4806 0.2866
## EEAA                              -0.5814 0.8763  -2.2989   1.1362 0.5071
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_PM_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1184 0.7164  -1.2858   1.5226 0.8687
## AgeAccelerationResidualHannum     -0.8038 0.6292  -2.0370   0.4294 0.2014
## AgeAccelPheno                     -1.4086 0.5999  -2.5843  -0.2328 0.0189
## DNAmAgeSkinBloodClockAdjAge       -1.2856 0.6607  -2.5806   0.0094 0.0517
## AgeAccelGrim                       0.0996 0.4475  -0.7774   0.9767 0.8238
## DNAmTLAdjAge                       0.0203 0.0301  -0.0387   0.0793 0.5002
## IEAA                               0.4365 0.6702  -0.8772   1.7501 0.5149
## EEAA                              -0.9111 0.8335  -2.5447   0.7225 0.2743
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_NO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.6432 0.5327  -0.4008   1.6872 0.2272
## AgeAccelerationResidualHannum      0.1791 0.4689  -0.7400   1.0982 0.7025
## AgeAccelPheno                      0.2979 0.4674  -0.6181   1.2140 0.5238
## DNAmAgeSkinBloodClockAdjAge        0.6087 0.4370  -0.2478   1.4651 0.1636
## AgeAccelGrim                       0.0661 0.3025  -0.5268   0.6590 0.8271
## DNAmTLAdjAge                       0.0158 0.0184  -0.0203   0.0519 0.3903
## IEAA                               0.4395 0.4653  -0.4725   1.3516 0.3449
## EEAA                               0.2371 0.5973  -0.9335   1.4078 0.6913
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.8092 0.6830  -2.1479   0.5295 0.2361
## AgeAccelerationResidualHannum     -0.8968 0.6305  -2.1326   0.3390 0.1549
## AgeAccelPheno                     -0.8388 0.4924  -1.8039   0.1263 0.0885
## DNAmAgeSkinBloodClockAdjAge       -1.0696 0.5518  -2.1511   0.0119 0.0526
## AgeAccelGrim                      -0.4987 0.2868  -1.0607   0.0634 0.0820
## DNAmTLAdjAge                       0.0189 0.0229  -0.0260   0.0638 0.4087
## IEAA                              -0.7972 0.5958  -1.9649   0.3706 0.1809
## EEAA                              -1.3413 0.7417  -2.7951   0.1124 0.0705
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Full model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1840       0.2968
## Hannum EAA     0.2914       0.3775
## PhenoAge EAA   0.0241       0.0755
## Skin&Blood EAA 0.0283       0.0755
## GrimAge EAA    0.0263       0.0755
## DNAmTL         0.4823       0.4823
## IEAA           0.3303       0.3775
## EEAA           0.1855       0.2968

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4226       0.4830
## Hannum EAA     0.1558       0.2707
## PhenoAge EAA   0.0209       0.1672
## Skin&Blood EAA 0.1692       0.2707
## GrimAge EAA    0.0806       0.2149
## DNAmTL         0.2510       0.3347
## IEAA           0.6041       0.6041
## EEAA           0.0626       0.2149

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0700       0.0800
## Hannum EAA     0.0037       0.0148
## PhenoAge EAA   0.0093       0.0248
## Skin&Blood EAA 0.0426       0.0800
## GrimAge EAA    0.0651       0.0800
## DNAmTL         0.2166       0.2166
## IEAA           0.0509       0.0800
## EEAA           0.0019       0.0148

GEE (No confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $CUR6_BC_PAH6
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.6520 0.5848  -1.7983   0.4942 0.2649
## AgeAccelerationResidualHannum     -0.3481 0.4333  -1.1973   0.5011 0.4217
## AgeAccelPheno                     -0.2974 0.4152  -1.1111   0.5163 0.4737
## DNAmAgeSkinBloodClockAdjAge       -0.1340 0.3940  -0.9063   0.6383 0.7338
## AgeAccelGrim                       0.4438 0.2635  -0.0726   0.9603 0.0921
## DNAmTLAdjAge                       0.0330 0.0199  -0.0059   0.0720 0.0965
## IEAA                              -0.3293 0.5098  -1.3286   0.6700 0.5184
## EEAA                              -0.6468 0.5313  -1.6883   0.3946 0.2235
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_PAH31
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1849 0.5865  -0.9646   1.3345 0.7525
## AgeAccelerationResidualHannum     -0.1720 0.4903  -1.1329   0.7890 0.7258
## AgeAccelPheno                     -0.0874 0.4312  -0.9325   0.7578 0.8395
## DNAmAgeSkinBloodClockAdjAge        0.2744 0.3991  -0.5077   1.0566 0.4916
## AgeAccelGrim                       0.8389 0.2723   0.3051   1.3727 0.0021
## DNAmTLAdjAge                      -0.0048 0.0184  -0.0408   0.0312 0.7945
## IEAA                               0.1455 0.6018  -1.0340   1.3249 0.8090
## EEAA                              -0.2466 0.5932  -1.4093   0.9161 0.6776
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0032 0.5124  -1.0011   1.0076 0.9950
## AgeAccelerationResidualHannum     -0.2888 0.4195  -1.1110   0.5335 0.4912
## AgeAccelPheno                     -0.4877 0.4246  -1.3200   0.3446 0.2508
## DNAmAgeSkinBloodClockAdjAge       -0.1578 0.3648  -0.8727   0.5572 0.6654
## AgeAccelGrim                       0.5243 0.2651   0.0046   1.0440 0.0480
## DNAmTLAdjAge                      -0.0325 0.0183  -0.0684   0.0034 0.0759
## IEAA                              -0.0481 0.4058  -0.8434   0.7472 0.9056
## EEAA                              -0.2151 0.5626  -1.3178   0.8877 0.7023
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_PM_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.3099 0.5359  -0.7404   1.3603 0.5630
## AgeAccelerationResidualHannum     -0.5576 0.4625  -1.4641   0.3489 0.2280
## AgeAccelPheno                     -0.7742 0.4994  -1.7530   0.2047 0.1211
## DNAmAgeSkinBloodClockAdjAge       -0.5419 0.5002  -1.5222   0.4385 0.2787
## AgeAccelGrim                       0.8264 0.4161   0.0109   1.6419 0.0470
## DNAmTLAdjAge                      -0.0027 0.0235  -0.0488   0.0434 0.9087
## IEAA                               0.5748 0.4840  -0.3739   1.5235 0.2350
## EEAA                              -0.6345 0.6319  -1.8729   0.6040 0.3153
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_NO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.4583 0.4309  -0.3863   1.3030 0.2875
## AgeAccelerationResidualHannum     -0.0198 0.3680  -0.7410   0.7015 0.9572
## AgeAccelPheno                      0.2655 0.3812  -0.4817   1.0128 0.4861
## DNAmAgeSkinBloodClockAdjAge        0.2463 0.3083  -0.3580   0.8506 0.4243
## AgeAccelGrim                       0.1308 0.3115  -0.4797   0.7413 0.6745
## DNAmTLAdjAge                       0.0010 0.0156  -0.0295   0.0316 0.9478
## IEAA                               0.4146 0.4083  -0.3857   1.2149 0.3099
## EEAA                              -0.0830 0.4994  -1.0618   0.8959 0.8680
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUR6_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.3123 0.5157  -1.3230   0.6985 0.5448
## AgeAccelerationResidualHannum     -0.3725 0.5345  -1.4200   0.6751 0.4859
## AgeAccelPheno                     -0.1697 0.5410  -1.2300   0.8906 0.7538
## DNAmAgeSkinBloodClockAdjAge       -0.4321 0.4484  -1.3110   0.4468 0.3352
## AgeAccelGrim                      -0.4874 0.3124  -1.0997   0.1250 0.1188
## DNAmTLAdjAge                      -0.0070 0.0195  -0.0452   0.0311 0.7174
## IEAA                              -0.4773 0.4994  -1.4561   0.5016 0.3393
## EEAA                              -0.5823 0.6174  -1.7923   0.6278 0.3456
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

3.2. Clusters based on model-based exposure estimates accrued before age 18 (clusCHLD5)

The file “LEX_clusCHLD5.csv” has information on estimated pollutant exposures during early childhood. Estimates are available for 5 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CHLD5_X7 – a cluster of 7 air pollutants
CHLD5_X33 – a large cluster of 33 air pollutants
CHLD5_NkF – NkF only
CHLD5_NO2 – NO2 only
CHLD5_SO2 – SO2 only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
CHLD5_X7 0.09 (-0.5, 0.5) -0.63 (-0.9, -0.1) 0.10 (-0.5, 0.3) 0.86 (0.7, 1.1)
(Missing) 3 2 1 0
CHLD5_X33 0.23 (-0.7, 1.1) -0.83 (-1.4, 0.1) 0.51 (-0.4, 1.2) 0.95 (-0.1, 1.0)
(Missing) 3 2 1 0
CHLD5_NkF -0.21 (-0.8, 0.7) 0.06 (-0.3, 0.7) -0.45 (-1.0, 0.5) 1.07 (0.5, 1.5)
(Missing) 3 2 1 0
CHLD5_NO2 0.34 (-0.5, 0.8) 0.17 (-0.5, 0.9) 0.43 (-0.6, 0.8) -0.21 (-0.3, 0.2)
(Missing) 3 2 1 0
CHLD5_SO2 0.34 (-0.7, 0.4) 0.45 (0.3, 1.4) 0.34 (-0.9, 0.4) 0.22 (-0.2, 0.3)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7432       0.7432
## Hannum EAA     0.0942       0.1884
## PhenoAge EAA   0.0854       0.1884
## Skin&Blood EAA 0.1751       0.2802
## GrimAge EAA    0.0062       0.0496
## DNAmTL         0.7396       0.7432
## IEAA           0.5899       0.7432
## EEAA           0.0758       0.1884

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $CHLD5_X7
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.3917 0.6187  -0.8211   1.6044 0.5267
## AgeAccelerationResidualHannum      0.7213 0.5532  -0.3629   1.8056 0.1923
## AgeAccelPheno                      0.3717 0.4627  -0.5352   1.2785 0.4218
## DNAmAgeSkinBloodClockAdjAge        0.2363 0.5132  -0.7695   1.2422 0.6452
## AgeAccelGrim                       0.8467 0.2817   0.2945   1.3989 0.0027
## DNAmTLAdjAge                      -0.0045 0.0223  -0.0481   0.0392 0.8412
## IEAA                               0.2322 0.5680  -0.8812   1.3455 0.6827
## EEAA                               1.1347 0.6785  -0.1951   2.4645 0.0944
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_X33
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1259 0.5224  -0.8980   1.1498 0.8095
## AgeAccelerationResidualHannum      0.8465 0.5130  -0.1590   1.8519 0.0989
## AgeAccelPheno                      1.1737 0.4197   0.3512   1.9963 0.0052
## DNAmAgeSkinBloodClockAdjAge        0.8201 0.4060   0.0243   1.6159 0.0434
## AgeAccelGrim                       0.9856 0.2866   0.4240   1.5473 0.0006
## DNAmTLAdjAge                      -0.0027 0.0198  -0.0415   0.0362 0.8937
## IEAA                              -0.3685 0.5105  -1.3691   0.6320 0.4704
## EEAA                               1.1985 0.6145  -0.0060   2.4029 0.0511
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.01
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                   <= 0.001
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1931 0.3349  -0.4633   0.8496 0.5642
## AgeAccelerationResidualHannum      0.2603 0.2862  -0.3007   0.8213 0.3631
## AgeAccelPheno                     -0.1291 0.3552  -0.8254   0.5672 0.7163
## DNAmAgeSkinBloodClockAdjAge       -0.1611 0.3120  -0.7727   0.4504 0.6056
## AgeAccelGrim                       0.4115 0.2526  -0.0836   0.9066 0.1033
## DNAmTLAdjAge                      -0.0192 0.0192  -0.0568   0.0184 0.3158
## IEAA                               0.0420 0.3012  -0.5483   0.6323 0.8892
## EEAA                               0.3784 0.3929  -0.3917   1.1485 0.3355
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_NO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0696 0.3649  -0.7848   0.6456 0.8488
## AgeAccelerationResidualHannum      0.2628 0.3637  -0.4502   0.9757 0.4700
## AgeAccelPheno                      0.3558 0.3436  -0.3176   1.0292 0.3004
## DNAmAgeSkinBloodClockAdjAge        0.4472 0.2986  -0.1381   1.0326 0.1343
## AgeAccelGrim                      -0.0598 0.2119  -0.4750   0.3555 0.7779
## DNAmTLAdjAge                      -0.0064 0.0147  -0.0353   0.0225 0.6655
## IEAA                              -0.1213 0.3125  -0.7338   0.4911 0.6978
## EEAA                               0.3002 0.4426  -0.5673   1.1677 0.4976
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1094 0.6905  -1.2439   1.4627 0.8741
## AgeAccelerationResidualHannum      0.3280 0.5959  -0.8399   1.4959 0.5820
## AgeAccelPheno                      0.6419 0.5146  -0.3667   1.6506 0.2123
## DNAmAgeSkinBloodClockAdjAge        0.2753 0.5373  -0.7779   1.3285 0.6084
## AgeAccelGrim                      -0.0645 0.3084  -0.6690   0.5399 0.8343
## DNAmTLAdjAge                       0.0120 0.0229  -0.0329   0.0568 0.6005
## IEAA                              -0.0170 0.5925  -1.1783   1.1442 0.9771
## EEAA                               0.2154 0.7413  -1.2376   1.6684 0.7714
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

GEE (Mix, mutual adjust)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X7 + \beta_2 X33 + \beta_3 NkF + \beta_4 NO2 + \beta_5 SO2 \\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_9 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## The estimated effects:
## $CHLD5_X7
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.6904 1.0225  -1.3137   2.6945 0.4995
## AgeAccelerationResidualHannum      0.6243 0.8139  -0.9710   2.2195 0.4431
## AgeAccelPheno                     -0.1287 0.7865  -1.6703   1.4128 0.8700
## DNAmAgeSkinBloodClockAdjAge       -0.1970 0.8598  -1.8822   1.4881 0.8188
## AgeAccelGrim                       0.1983 0.4228  -0.6304   1.0269 0.6391
## DNAmTLAdjAge                       0.0165 0.0338  -0.0497   0.0827 0.6248
## IEAA                               1.0194 0.8572  -0.6608   2.6995 0.2344
## EEAA                               0.9046 1.0422  -1.1381   2.9472 0.3854
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_X33
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2729 0.8576  -1.9538   1.4079 0.7503
## AgeAccelerationResidualHannum      0.4460 0.6396  -0.8077   1.6996 0.4856
## AgeAccelPheno                      1.2061 0.6459  -0.0599   2.4721 0.0619
## DNAmAgeSkinBloodClockAdjAge        0.8990 0.6841  -0.4418   2.2398 0.1888
## AgeAccelGrim                       0.8611 0.3610   0.1536   1.5686 0.0171
## DNAmTLAdjAge                      -0.0106 0.0297  -0.0688   0.0475 0.7198
## IEAA                              -0.9280 0.7195  -2.3382   0.4822 0.1971
## EEAA                               0.6388 0.8157  -0.9600   2.2377 0.4335
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0648 0.3457  -0.6127   0.7423 0.8513
## AgeAccelerationResidualHannum      0.1630 0.3172  -0.4588   0.7848 0.6073
## AgeAccelPheno                     -0.0802 0.3644  -0.7945   0.6340 0.8257
## DNAmAgeSkinBloodClockAdjAge       -0.0941 0.2975  -0.6771   0.4889 0.7518
## AgeAccelGrim                       0.3179 0.2550  -0.1819   0.8177 0.2125
## DNAmTLAdjAge                      -0.0227 0.0206  -0.0631   0.0178 0.2717
## IEAA                              -0.1412 0.3156  -0.7598   0.4775 0.6547
## EEAA                               0.2071 0.4338  -0.6432   1.0573 0.6331
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_NO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0677 0.4255  -0.9017   0.7662 0.8735
## AgeAccelerationResidualHannum      0.1980 0.3563  -0.5003   0.8964 0.5783
## AgeAccelPheno                      0.0596 0.3624  -0.6507   0.7699 0.8693
## DNAmAgeSkinBloodClockAdjAge        0.3526 0.3091  -0.2532   0.9583 0.2540
## AgeAccelGrim                      -0.0558 0.2133  -0.4738   0.3623 0.7938
## DNAmTLAdjAge                      -0.0142 0.0186  -0.0506   0.0223 0.4471
## IEAA                              -0.0383 0.3904  -0.8036   0.7269 0.9218
## EEAA                               0.2944 0.4404  -0.5687   1.1576 0.5038
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.4651 0.8224  -1.1467   2.0769 0.5717
## AgeAccelerationResidualHannum      0.4725 0.7251  -0.9487   1.8936 0.5147
## AgeAccelPheno                      0.4258 0.5653  -0.6821   1.5337 0.4513
## DNAmAgeSkinBloodClockAdjAge       -0.0995 0.5498  -1.1770   0.9780 0.8564
## AgeAccelGrim                       0.0243 0.3633  -0.6878   0.7364 0.9467
## DNAmTLAdjAge                       0.0230 0.0298  -0.0353   0.0813 0.4396
## IEAA                               0.4772 0.7482  -0.9893   1.9437 0.5236
## EEAA                               0.4131 0.8874  -1.3263   2.1524 0.6416
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8864       0.8864
## Hannum EAA     0.2901       0.5840
## PhenoAge EAA   0.1416       0.5664
## Skin&Blood EAA 0.3650       0.5840
## GrimAge EAA    0.0208       0.1664
## DNAmTL         0.5466       0.7288
## IEAA           0.6847       0.7825
## EEAA           0.3074       0.5840

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9805       0.9805
## Hannum EAA     0.3840       0.7341
## PhenoAge EAA   0.0700       0.2800
## Skin&Blood EAA 0.1867       0.4979
## GrimAge EAA    0.0634       0.2800
## DNAmTL         0.5506       0.7341
## IEAA           0.7515       0.8589
## EEAA           0.4588       0.7341

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9268       0.9268
## Hannum EAA     0.3979       0.6366
## PhenoAge EAA   0.0655       0.3264
## Skin&Blood EAA 0.2424       0.6366
## GrimAge EAA    0.0816       0.3264
## DNAmTL         0.3621       0.6366
## IEAA           0.7955       0.9091
## EEAA           0.4873       0.6497

GEE (no confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $CHLD5_X7
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.2300 0.6355  -1.0156   1.4756 0.7175
## AgeAccelerationResidualHannum      0.6428 0.5141  -0.3647   1.6504 0.2111
## AgeAccelPheno                      0.3259 0.4793  -0.6135   1.2652 0.4965
## DNAmAgeSkinBloodClockAdjAge        0.1892 0.5195  -0.8290   1.2073 0.7157
## AgeAccelGrim                       0.7565 0.3067   0.1554   1.3576 0.0136
## DNAmTLAdjAge                      -0.0004 0.0214  -0.0422   0.0415 0.9869
## IEAA                               0.1529 0.5995  -1.0221   1.3279 0.7987
## EEAA                               0.9809 0.6373  -0.2683   2.2300 0.1238
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_X33
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0284 0.4894  -0.9875   0.9307 0.9537
## AgeAccelerationResidualHannum      0.6433 0.4653  -0.2686   1.5552 0.1668
## AgeAccelPheno                      0.9367 0.3985   0.1556   1.7178 0.0188
## DNAmAgeSkinBloodClockAdjAge        0.6423 0.3643  -0.0718   1.3564 0.0779
## AgeAccelGrim                       0.7756 0.2960   0.1954   1.3558 0.0088
## DNAmTLAdjAge                       0.0060 0.0177  -0.0287   0.0406 0.7361
## IEAA                              -0.3400 0.4841  -1.2889   0.6089 0.4825
## EEAA                               0.8631 0.5639  -0.2422   1.9683 0.1259
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.2009 0.3508  -0.4867   0.8885 0.5669
## AgeAccelerationResidualHannum      0.2858 0.2990  -0.3002   0.8718 0.3390
## AgeAccelPheno                     -0.0177 0.3683  -0.7395   0.7041 0.9617
## DNAmAgeSkinBloodClockAdjAge       -0.1422 0.2889  -0.7085   0.4242 0.6227
## AgeAccelGrim                       0.4860 0.2742  -0.0515   1.0235 0.0764
## DNAmTLAdjAge                      -0.0246 0.0172  -0.0582   0.0091 0.1520
## IEAA                               0.0679 0.3115  -0.5425   0.6784 0.8273
## EEAA                               0.3955 0.4037  -0.3958   1.1869 0.3272
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_NO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2704 0.3266  -0.9105   0.3698 0.4078
## AgeAccelerationResidualHannum      0.1426 0.3101  -0.4653   0.7505 0.6457
## AgeAccelPheno                      0.4990 0.3155  -0.1194   1.1174 0.1137
## DNAmAgeSkinBloodClockAdjAge        0.3625 0.2584  -0.1440   0.8690 0.1607
## AgeAccelGrim                      -0.0149 0.1829  -0.3734   0.3436 0.9349
## DNAmTLAdjAge                      -0.0077 0.0140  -0.0352   0.0197 0.5795
## IEAA                              -0.1196 0.2780  -0.6646   0.4253 0.6670
## EEAA                               0.0513 0.3955  -0.7239   0.8265 0.8968
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CHLD5_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2091 0.6424  -1.4682   1.0500 0.7448
## AgeAccelerationResidualHannum      0.2457 0.4857  -0.7062   1.1976 0.6129
## AgeAccelPheno                      0.7646 0.4714  -0.1594   1.6886 0.1048
## DNAmAgeSkinBloodClockAdjAge        0.2308 0.4446  -0.6406   1.1022 0.6037
## AgeAccelGrim                      -0.1219 0.2866  -0.6836   0.4399 0.6707
## DNAmTLAdjAge                       0.0078 0.0210  -0.0333   0.0490 0.7090
## IEAA                              -0.1002 0.5718  -1.2210   1.0205 0.8608
## EEAA                               0.0178 0.6093  -1.1765   1.2121 0.9767
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

3.3. Clusters based on model-based lifetime exposure estimates (clusCUM6)

The file “LEX_clus CUM6.csv” has information on estimated cumulative pollutant exposures during the lifecourse. Estimates are available for 6 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CUM6_BC_NO2_PM – a cluster of BC, NO2, and PM
CUM6_PAH36 – a large cluster of 36 PAHs
CUM6_DlP – DlP only
CUM6_NkF – NkF only
CUM6_RET – retene only
CUM6_SO2 – SO2 only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
CUM6_BC_NO2_PM 0.22 (-0.6, 0.8) 0.19 (-0.3, 0.7) 0.10 (-1.0, 0.8) 1.38 (0.4, 1.6)
(Missing) 3 2 1 0
CUM6_PAH36 0.25 (-0.6, 1.1) -1.00 (-1.2, -0.3) 0.32 (-0.5, 1.2) 0.83 (0.4, 1.4)
(Missing) 3 2 1 0
CUM6_DlP -0.48 (-1.0, 0.8) 0.65 (0.5, 1.1) -0.66 (-1.2, 0.7) 0.42 (0.3, 0.6)
(Missing) 3 2 1 0
CUM6_NkF -0.22 (-0.8, 0.5) -0.07 (-0.3, 0.4) -0.31 (-1.0, 0.4) 1.18 (0.1, 1.7)
(Missing) 3 2 1 0
CUM6_RET -0.22 (-0.7, 0.3) -0.41 (-0.9, 0.3) -0.25 (-0.8, 0.2) 1.71 (1.2, 1.9)
(Missing) 3 2 1 0
CUM6_SO2 0.09 (-0.4, 0.4) 1.13 (0.5, 1.6) -0.03 (-0.9, 0.3) -0.02 (-0.6, 0.1)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4352       0.4352
## Hannum EAA     0.3483       0.3981
## PhenoAge EAA   0.1067       0.3406
## Skin&Blood EAA 0.1656       0.3406
## GrimAge EAA    0.0003       0.0024
## DNAmTL         0.1703       0.3406
## IEAA           0.2877       0.3836
## EEAA           0.2428       0.3836

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $CUM6_BC_NO2_PM
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.4392 0.6311  -0.7979   1.6762 0.4865
## AgeAccelerationResidualHannum      0.8076 0.5831  -0.3353   1.9506 0.1661
## AgeAccelPheno                      0.2404 0.5971  -0.9300   1.4107 0.6873
## DNAmAgeSkinBloodClockAdjAge        0.0290 0.6440  -1.2332   1.2913 0.9640
## AgeAccelGrim                       0.8858 0.3820   0.1371   1.6345 0.0204
## DNAmTLAdjAge                      -0.0262 0.0205  -0.0663   0.0139 0.2010
## IEAA                               0.5180 0.6224  -0.7019   1.7378 0.4053
## EEAA                               1.2115 0.7200  -0.1997   2.6226 0.0924
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_PAH36
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.3210 0.6119  -0.8784   1.5204 0.5999
## AgeAccelerationResidualHannum      0.7980 0.5509  -0.2818   1.8778 0.1475
## AgeAccelPheno                      0.8835 0.4908  -0.0785   1.8455 0.0719
## DNAmAgeSkinBloodClockAdjAge        0.6827 0.4676  -0.2339   1.5993 0.1443
## AgeAccelGrim                       1.2678 0.2661   0.7462   1.7895 0.0000
## DNAmTLAdjAge                      -0.0178 0.0224  -0.0617   0.0262 0.4278
## IEAA                               0.1289 0.5821  -1.0121   1.2699 0.8247
## EEAA                               1.1098 0.6575  -0.1790   2.3986 0.0914
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                   <= 0.001
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_DlP
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.7756 0.5383  -0.2795   1.8306 0.1496
## AgeAccelerationResidualHannum      0.1327 0.5475  -0.9404   1.2058 0.8085
## AgeAccelPheno                      0.1314 0.4237  -0.6991   0.9619 0.7565
## DNAmAgeSkinBloodClockAdjAge        0.1594 0.4375  -0.6982   1.0170 0.7157
## AgeAccelGrim                      -0.5186 0.2508  -1.0102  -0.0270 0.0387
## DNAmTLAdjAge                      -0.0332 0.0204  -0.0731   0.0068 0.1037
## IEAA                               0.7906 0.4787  -0.1476   1.7288 0.0986
## EEAA                               0.3262 0.6815  -1.0095   1.6620 0.6322
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.2349 0.3700  -0.4904   0.9602 0.5256
## AgeAccelerationResidualHannum      0.1825 0.3847  -0.5715   0.9366 0.6352
## AgeAccelPheno                     -0.1376 0.4010  -0.9235   0.6484 0.7316
## DNAmAgeSkinBloodClockAdjAge       -0.1358 0.3705  -0.8619   0.5903 0.7139
## AgeAccelGrim                       0.6442 0.2341   0.1853   1.1031 0.0059
## DNAmTLAdjAge                      -0.0386 0.0179  -0.0736  -0.0035 0.0309
## IEAA                               0.1012 0.3225  -0.5309   0.7333 0.7536
## EEAA                               0.3622 0.5060  -0.6296   1.3541 0.4741
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                    <= 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.2835 0.5528  -0.7999   1.3669 0.6080
## AgeAccelerationResidualHannum     -0.0439 0.4393  -0.9049   0.8172 0.9205
## AgeAccelPheno                     -0.4557 0.4564  -1.3503   0.4388 0.3180
## DNAmAgeSkinBloodClockAdjAge       -0.3540 0.5068  -1.3473   0.6394 0.4849
## AgeAccelGrim                       0.7790 0.3676   0.0584   1.4995 0.0341
## DNAmTLAdjAge                      -0.0109 0.0214  -0.0529   0.0310 0.6095
## IEAA                               0.4603 0.4813  -0.4831   1.4037 0.3389
## EEAA                               0.0952 0.5937  -1.0685   1.2588 0.8726
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.1814 0.6632  -1.4812   1.1185 0.7845
## AgeAccelerationResidualHannum      0.0367 0.6422  -1.2219   1.2954 0.9544
## AgeAccelPheno                      0.2268 0.5123  -0.7773   1.2309 0.6580
## DNAmAgeSkinBloodClockAdjAge       -0.1203 0.5488  -1.1958   0.9553 0.8265
## AgeAccelGrim                      -0.3276 0.3323  -0.9790   0.3238 0.3242
## DNAmTLAdjAge                       0.0204 0.0237  -0.0260   0.0668 0.3897
## IEAA                              -0.0504 0.5732  -1.1739   1.0730 0.9299
## EEAA                              -0.2601 0.7587  -1.7472   1.2270 0.7317
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

GEE (Mix, mutual adjust)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * BC\_NO2\_PM + \beta_2 PAH36 + \beta_3 DlP + \beta_4 NkF + \beta_5 RET + \beta_6 SO2 \\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## The estimated effects:
## $CUM6_BC_NO2_PM
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.3294 0.6969  -1.6953   1.0365 0.6365
## AgeAccelerationResidualHannum      0.7298 0.7670  -0.7736   2.2332 0.3414
## AgeAccelPheno                     -0.4609 0.7514  -1.9336   1.0117 0.5396
## DNAmAgeSkinBloodClockAdjAge       -0.7105 0.7201  -2.1220   0.7009 0.3238
## AgeAccelGrim                       0.4986 0.4906  -0.4630   1.4602 0.3095
## DNAmTLAdjAge                      -0.0252 0.0304  -0.0848   0.0344 0.4070
## IEAA                              -0.2544 0.7423  -1.7092   1.2005 0.7318
## EEAA                               1.0705 0.9587  -0.8087   2.9496 0.2642
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_PAH36
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.9063 0.8081  -0.6777   2.4902 0.2621
## AgeAccelerationResidualHannum      0.7311 0.7755  -0.7889   2.2511 0.3458
## AgeAccelPheno                      1.9266 0.7208   0.5139   3.3394 0.0075
## DNAmAgeSkinBloodClockAdjAge        1.7831 0.7917   0.2313   3.3349 0.0243
## AgeAccelGrim                       0.5738 0.4859  -0.3785   1.5261 0.2376
## DNAmTLAdjAge                      -0.0070 0.0335  -0.0727   0.0587 0.8344
## IEAA                               0.5752 0.7796  -0.9529   2.1033 0.4607
## EEAA                               0.9721 0.9919  -0.9719   2.9162 0.3270
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.01
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_DlP
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            1.3988 0.6645   0.0964   2.7011 0.0353
## AgeAccelerationResidualHannum      0.1585 0.6288  -1.0739   1.3910 0.8010
## AgeAccelPheno                      0.8106 0.5817  -0.3295   1.9507 0.1635
## DNAmAgeSkinBloodClockAdjAge        0.9757 0.5683  -0.1381   2.0895 0.0860
## AgeAccelGrim                      -0.5896 0.3312  -1.2388   0.0596 0.0751
## DNAmTLAdjAge                      -0.0236 0.0259  -0.0744   0.0272 0.3627
## IEAA                               1.4119 0.5749   0.2850   2.5387 0.0141
## EEAA                               0.4488 0.8061  -1.1312   2.0289 0.5777
##                               sig_level
## AgeAccelerationResidual         <= 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                            <= 0.05
## EEAA                             > 0.05
## 
## $CUM6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.4727 0.4502  -1.3550   0.4096 0.2937
## AgeAccelerationResidualHannum      0.1441 0.4278  -0.6943   0.9826 0.7361
## AgeAccelPheno                     -0.4277 0.4670  -1.3430   0.4877 0.3598
## DNAmAgeSkinBloodClockAdjAge       -0.5165 0.4695  -1.4368   0.4037 0.2713
## AgeAccelGrim                       0.5851 0.2662   0.0633   1.1068 0.0280
## DNAmTLAdjAge                      -0.0370 0.0223  -0.0806   0.0066 0.0962
## IEAA                              -0.7212 0.3899  -1.4854   0.0429 0.0643
## EEAA                               0.1993 0.5869  -0.9511   1.3498 0.7341
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.4199 0.7076  -0.9670   1.8067 0.5529
## AgeAccelerationResidualHannum     -0.7094 0.6050  -1.8953   0.4764 0.2410
## AgeAccelPheno                     -0.6785 0.6227  -1.8991   0.5420 0.2759
## DNAmAgeSkinBloodClockAdjAge       -0.4159 0.5630  -1.5194   0.6876 0.4601
## AgeAccelGrim                      -0.0469 0.4041  -0.8389   0.7450 0.9075
## DNAmTLAdjAge                       0.0255 0.0262  -0.0259   0.0770 0.3309
## IEAA                               0.8649 0.5691  -0.2505   1.9803 0.1286
## EEAA                              -0.8810 0.7723  -2.3947   0.6327 0.2540
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.3489 0.6759  -1.6737   0.9759 0.6057
## AgeAccelerationResidualHannum     -0.1683 0.6773  -1.4958   1.1592 0.8038
## AgeAccelPheno                      0.0488 0.5180  -0.9665   1.0641 0.9249
## DNAmAgeSkinBloodClockAdjAge       -0.2951 0.5765  -1.4250   0.8347 0.6087
## AgeAccelGrim                      -0.1468 0.2896  -0.7144   0.4209 0.6124
## DNAmTLAdjAge                       0.0337 0.0240  -0.0135   0.0808 0.1614
## IEAA                              -0.1429 0.5625  -1.2455   0.9597 0.7994
## EEAA                              -0.5937 0.8232  -2.2073   1.0198 0.4708
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5551       0.7401
## Hannum EAA     0.8488       0.8488
## PhenoAge EAA   0.1559       0.4157
## Skin&Blood EAA 0.2862       0.5202
## GrimAge EAA    0.0170       0.1360
## DNAmTL         0.1043       0.4157
## IEAA           0.3251       0.5202
## EEAA           0.7581       0.8488

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6381       0.6381
## Hannum EAA     0.5536       0.6342
## PhenoAge EAA   0.0248       0.1984
## Skin&Blood EAA 0.1313       0.2626
## GrimAge EAA    0.1039       0.2626
## DNAmTL         0.0790       0.2626
## IEAA           0.4141       0.6342
## EEAA           0.5549       0.6342

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6380       0.6380
## Hannum EAA     0.3701       0.4914
## PhenoAge EAA   0.0243       0.1944
## Skin&Blood EAA 0.0878       0.2054
## GrimAge EAA    0.1027       0.2054
## DNAmTL         0.0826       0.2054
## IEAA           0.4300       0.4914
## EEAA           0.3562       0.4914

GEE (no confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $CUM6_BC_NO2_PM
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.1734 0.4456  -1.0467   0.6999 0.6971
## AgeAccelerationResidualHannum      0.4721 0.3234  -0.1618   1.1060 0.1444
## AgeAccelPheno                      0.3496 0.4102  -0.4544   1.1536 0.3941
## DNAmAgeSkinBloodClockAdjAge       -0.0335 0.3769  -0.7723   0.7052 0.9291
## AgeAccelGrim                       0.4367 0.2690  -0.0905   0.9639 0.1045
## DNAmTLAdjAge                      -0.0209 0.0177  -0.0557   0.0138 0.2375
## IEAA                               0.1711 0.4039  -0.6205   0.9627 0.6718
## EEAA                               0.5373 0.4342  -0.3137   1.3882 0.2159
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_PAH36
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.1126 0.4644  -1.0228   0.7975 0.8084
## AgeAccelerationResidualHannum      0.5499 0.3995  -0.2330   1.3329 0.1686
## AgeAccelPheno                      0.6414 0.3990  -0.1406   1.4235 0.1079
## DNAmAgeSkinBloodClockAdjAge        0.4022 0.3552  -0.2940   1.0983 0.2575
## AgeAccelGrim                       0.7544 0.2700   0.2252   1.2837 0.0052
## DNAmTLAdjAge                      -0.0101 0.0178  -0.0450   0.0248 0.5721
## IEAA                              -0.0190 0.4417  -0.8847   0.8468 0.9658
## EEAA                               0.6339 0.4885  -0.3236   1.5914 0.1944
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_DlP
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.4152 0.4627  -0.4917   1.3221 0.3695
## AgeAccelerationResidualHannum      0.1178 0.4132  -0.6920   0.9277 0.7755
## AgeAccelPheno                      0.4277 0.3622  -0.2821   1.1376 0.2376
## DNAmAgeSkinBloodClockAdjAge        0.1169 0.3244  -0.5188   0.7527 0.7184
## AgeAccelGrim                      -0.1473 0.2265  -0.5913   0.2968 0.5157
## DNAmTLAdjAge                      -0.0365 0.0141  -0.0641  -0.0089 0.0096
## IEAA                               0.5897 0.4160  -0.2256   1.4051 0.1563
## EEAA                               0.1673 0.5172  -0.8464   1.1810 0.7463
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                    <= 0.01
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1120 0.3737  -0.6205   0.8445 0.7644
## AgeAccelerationResidualHannum      0.2033 0.3729  -0.5276   0.9342 0.5856
## AgeAccelPheno                      0.0495 0.3930  -0.7208   0.8197 0.8999
## DNAmAgeSkinBloodClockAdjAge       -0.1305 0.3274  -0.7723   0.5112 0.6901
## AgeAccelGrim                       0.6356 0.2405   0.1642   1.1070 0.0082
## DNAmTLAdjAge                      -0.0408 0.0151  -0.0704  -0.0112 0.0069
## IEAA                               0.1163 0.3180  -0.5070   0.7395 0.7147
## EEAA                               0.2987 0.4929  -0.6673   1.2647 0.5445
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                    <= 0.01
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0812 0.5705  -1.0370   1.1995 0.8868
## AgeAccelerationResidualHannum     -0.0428 0.4251  -0.8759   0.7903 0.9198
## AgeAccelPheno                     -0.3113 0.4603  -1.2136   0.5910 0.4989
## DNAmAgeSkinBloodClockAdjAge       -0.3598 0.4878  -1.3160   0.5964 0.4608
## AgeAccelGrim                       0.7351 0.3803  -0.0103   1.4805 0.0532
## DNAmTLAdjAge                      -0.0127 0.0207  -0.0533   0.0279 0.5400
## IEAA                               0.4026 0.4841  -0.5463   1.3516 0.4056
## EEAA                              -0.0053 0.5756  -1.1335   1.1230 0.9927
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $CUM6_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.5240 0.5532  -1.6082   0.5602 0.3435
## AgeAccelerationResidualHannum      0.0312 0.4619  -0.8742   0.9366 0.9461
## AgeAccelPheno                      0.4436 0.4352  -0.4094   1.2965 0.3081
## DNAmAgeSkinBloodClockAdjAge       -0.0997 0.4221  -0.9269   0.7276 0.8133
## AgeAccelGrim                      -0.3178 0.2644  -0.8360   0.2004 0.2293
## DNAmTLAdjAge                       0.0085 0.0193  -0.0294   0.0463 0.6616
## IEAA                              -0.1665 0.4981  -1.1428   0.8097 0.7381
## EEAA                              -0.3398 0.5481  -1.4140   0.7345 0.5353
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

3.4. Clusters based on pollutant measurements (clusMEAS6)

The file “LEX_clusMEAS6.csv” has information on measured pollutant exposures during each visit. Estimates are available for 6 different prototypes (cluster variables) for a total of 54 subjects and 54 visits. The prototypes are labelled as:

MEAS6_BC_ PM_RET – a cluster of BC, PM, and retene
MEAS6_X31 – a large cluster of 31 air pollutants
MEAS6_X5 – a smaller cluster of 5 air pollutants
MEAS6_DlP – DlP only
MEAS6_NkF – NkF only
MEAS6_ NO2_SO2 – NO2, and SO2

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
MEAS6_BC_PM_RET 0.05 (-0.6, 0.5) -0.40 (-1.6, -0.3) 0.07 (-0.5, 0.5) 1.08 (0.5, 2.1)
(Missing) 70 10 57 3
MEAS6_X31 0.19 (-0.6, 0.7) -1.02 (-1.8, -0.8) 0.31 (-0.1, 0.8) 0.35 (-0.5, 0.8)
(Missing) 70 10 57 3
MEAS6_X5 -0.14 (-1.0, 1.0) -1.07 (-1.1, -1.0) 0.46 (-0.8, 1.1) 0.55 (-0.1, 0.9)
(Missing) 70 10 57 3
MEAS6_DlP -0.63 (-0.7, 1.3) 0.35 (-0.6, 1.0) -0.69 (-0.7, 1.2) -0.30 (-0.5, 1.3)
(Missing) 70 10 57 3
MEAS6_NkF -0.50 (-0.6, 1.2) -0.39 (-0.6, 0.6) -0.50 (-0.6, 1.2) -0.50 (-0.7, 0.2)
(Missing) 70 10 57 3
MEAS6_NO2_SO2 -0.08 (-0.9, 0.8) 0.98 (0.5, 1.5) -0.37 (-0.9, 0.8) -0.37 (-1.3, 0.2)
(Missing) 70 10 57 3
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0638       0.1542
## Hannum EAA     0.0771       0.1542
## PhenoAge EAA   0.1075       0.1720
## Skin&Blood EAA 0.0064       0.0512
## GrimAge EAA    0.2722       0.3111
## DNAmTL         0.4302       0.4302
## IEAA           0.2396       0.3111
## EEAA           0.0473       0.1542

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $MEAS6_BC_PM_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2296 0.7304  -1.6613   1.2020 0.7533
## AgeAccelerationResidualHannum     -0.3400 0.5285  -1.3759   0.6958 0.5200
## AgeAccelPheno                     -0.3903 0.7498  -1.8598   1.0792 0.6027
## DNAmAgeSkinBloodClockAdjAge        0.0186 0.5444  -1.0484   1.0857 0.9727
## AgeAccelGrim                       0.9907 0.5954  -0.1763   2.1577 0.0961
## DNAmTLAdjAge                       0.0158 0.0363  -0.0554   0.0869 0.6638
## IEAA                              -0.2816 0.5814  -1.4212   0.8580 0.6282
## EEAA                              -0.6547 0.7138  -2.0538   0.7444 0.3590
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_X31
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            1.4360 0.6955   0.0728   2.7992 0.0389
## AgeAccelerationResidualHannum      1.1210 0.6111  -0.0767   2.3187 0.0666
## AgeAccelPheno                      1.1073 0.7156  -0.2953   2.5099 0.1218
## DNAmAgeSkinBloodClockAdjAge        1.4220 0.5950   0.2558   2.5882 0.0169
## AgeAccelGrim                       0.9940 0.3785   0.2522   1.7358 0.0086
## DNAmTLAdjAge                      -0.0234 0.0252  -0.0727   0.0260 0.3530
## IEAA                               0.7468 0.5617  -0.3542   1.8478 0.1837
## EEAA                               1.1480 0.6803  -0.1853   2.4813 0.0915
##                               sig_level
## AgeAccelerationResidual         <= 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_X5
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0783 0.8406  -1.5693   1.7260 0.9257
## AgeAccelerationResidualHannum     -0.3408 0.7930  -1.8950   1.2135 0.6674
## AgeAccelPheno                      0.1365 0.7924  -1.4166   1.6897 0.8632
## DNAmAgeSkinBloodClockAdjAge        0.9817 0.6831  -0.3571   2.3206 0.1507
## AgeAccelGrim                       0.7447 0.4516  -0.1404   1.6299 0.0991
## DNAmTLAdjAge                       0.0274 0.0307  -0.0328   0.0876 0.3717
## IEAA                               0.2885 0.7009  -1.0854   1.6623 0.6806
## EEAA                              -0.9744 0.9620  -2.8599   0.9112 0.3111
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_DlP
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.2163 0.8624  -1.4740   1.9066 0.8019
## AgeAccelerationResidualHannum     -0.0537 0.7353  -1.4948   1.3875 0.9418
## AgeAccelPheno                      0.8796 0.7762  -0.6418   2.4010 0.2571
## DNAmAgeSkinBloodClockAdjAge       -1.2744 0.6524  -2.5531   0.0044 0.0508
## AgeAccelGrim                      -0.0727 0.5791  -1.2078   1.0623 0.9001
## DNAmTLAdjAge                      -0.0217 0.0336  -0.0876   0.0441 0.5176
## IEAA                               0.3652 0.6620  -0.9323   1.6626 0.5812
## EEAA                              -0.1406 0.9467  -1.9961   1.7150 0.8820
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0266 0.7390  -1.4219   1.4751 0.9713
## AgeAccelerationResidualHannum      0.5171 0.6153  -0.6888   1.7230 0.4006
## AgeAccelPheno                     -0.4109 0.6884  -1.7603   0.9384 0.5506
## DNAmAgeSkinBloodClockAdjAge        0.1726 0.5861  -0.9760   1.3213 0.7683
## AgeAccelGrim                      -0.2478 0.4258  -1.0825   0.5868 0.5606
## DNAmTLAdjAge                      -0.0070 0.0298  -0.0654   0.0513 0.8132
## IEAA                              -0.3908 0.6749  -1.7137   0.9321 0.5626
## EEAA                               0.8254 0.8254  -0.7923   2.4432 0.3173
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_NO2_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.8202 0.7289  -2.2488   0.6085 0.2605
## AgeAccelerationResidualHannum      0.5337 0.5810  -0.6052   1.6725 0.3584
## AgeAccelPheno                      0.7834 0.6617  -0.5134   2.0803 0.2364
## DNAmAgeSkinBloodClockAdjAge       -0.2823 0.5153  -1.2923   0.7276 0.5837
## AgeAccelGrim                      -0.0630 0.4900  -1.0233   0.8973 0.8977
## DNAmTLAdjAge                      -0.0436 0.0339  -0.1100   0.0229 0.1986
## IEAA                              -0.9758 0.5843  -2.1209   0.1694 0.0949
## EEAA                               0.2386 0.7346  -1.2013   1.6784 0.7454
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

GEE (Mix, mutual adjust)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF + \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## The estimated effects:
## $MEAS6_BC_PM_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -1.6878 1.0912  -3.8266   0.4511 0.1219
## AgeAccelerationResidualHannum     -1.1844 0.8687  -2.8871   0.5183 0.1728
## AgeAccelPheno                     -1.2972 0.9505  -3.1601   0.5656 0.1723
## DNAmAgeSkinBloodClockAdjAge       -1.8335 0.8283  -3.4570  -0.2100 0.0269
## AgeAccelGrim                       0.5460 0.8198  -1.0609   2.1528 0.5055
## DNAmTLAdjAge                       0.0185 0.0483  -0.0763   0.1132 0.7024
## IEAA                              -1.3573 0.8971  -3.1156   0.4011 0.1303
## EEAA                              -1.4932 1.0864  -3.6225   0.6362 0.1693
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_X31
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            3.5341 1.1395   1.3006   5.7676 0.0019
## AgeAccelerationResidualHannum      2.8712 0.7538   1.3938   4.3486 0.0001
## AgeAccelPheno                      2.5713 0.9291   0.7503   4.3924 0.0056
## DNAmAgeSkinBloodClockAdjAge        2.8039 0.7099   1.4124   4.1954 0.0001
## AgeAccelGrim                       1.1723 0.6026  -0.0087   2.3534 0.0517
## DNAmTLAdjAge                      -0.0770 0.0383  -0.1522  -0.0019 0.0446
## IEAA                               1.9296 1.0077  -0.0455   3.9046 0.0555
## EEAA                               3.7235 0.9840   1.7948   5.6521 0.0002
##                               sig_level
## AgeAccelerationResidual         <= 0.01
## AgeAccelerationResidualHannum  <= 0.001
## AgeAccelPheno                   <= 0.01
## DNAmAgeSkinBloodClockAdjAge    <= 0.001
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                    <= 0.05
## IEAA                             > 0.05
## EEAA                           <= 0.001
## 
## $MEAS6_X5
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -1.7383 1.3477  -4.3798   0.9033 0.1971
## AgeAccelerationResidualHannum     -1.7848 1.2037  -4.1439   0.5744 0.1381
## AgeAccelPheno                     -1.1187 1.0756  -3.2268   0.9894 0.2983
## DNAmAgeSkinBloodClockAdjAge       -0.2191 0.8492  -1.8834   1.4453 0.7964
## AgeAccelGrim                      -0.5506 0.9704  -2.4526   1.3515 0.5705
## DNAmTLAdjAge                       0.0735 0.0505  -0.0255   0.1724 0.1454
## IEAA                              -0.5405 1.1742  -2.8419   1.7610 0.6453
## EEAA                              -2.8972 1.4724  -5.7831  -0.0114 0.0491
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                            <= 0.05
## 
## $MEAS6_DlP
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.5500 0.7252  -1.9715   0.8714 0.4482
## AgeAccelerationResidualHannum     -0.7140 0.5672  -1.8258   0.3978 0.2081
## AgeAccelPheno                      0.3237 0.6517  -0.9537   1.6011 0.6194
## DNAmAgeSkinBloodClockAdjAge       -1.7802 0.5331  -2.8250  -0.7354 0.0008
## AgeAccelGrim                      -0.2730 0.4581  -1.1709   0.6248 0.5512
## DNAmTLAdjAge                      -0.0016 0.0274  -0.0554   0.0521 0.9526
## IEAA                              -0.0277 0.6383  -1.2787   1.2234 0.9654
## EEAA                              -1.0554 0.6892  -2.4062   0.2953 0.1257
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge    <= 0.001
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -1.1598 0.8232  -2.7733   0.4537 0.1589
## AgeAccelerationResidualHannum     -0.3325 0.6884  -1.6818   1.0168 0.6291
## AgeAccelPheno                     -1.0329 0.6362  -2.2799   0.2141 0.1045
## DNAmAgeSkinBloodClockAdjAge       -0.4344 0.5534  -1.5192   0.6504 0.4325
## AgeAccelGrim                      -0.6398 0.5874  -1.7911   0.5115 0.2761
## DNAmTLAdjAge                       0.0175 0.0320  -0.0453   0.0802 0.5854
## IEAA                              -1.0295 0.8361  -2.6682   0.6091 0.2182
## EEAA                              -0.4770 0.8629  -2.1682   1.2143 0.5804
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_NO2_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -1.1647 0.6790  -2.4956   0.1662 0.0863
## AgeAccelerationResidualHannum      0.4105 0.5214  -0.6115   1.4326 0.4311
## AgeAccelPheno                      0.4768 0.6388  -0.7753   1.7288 0.4555
## DNAmAgeSkinBloodClockAdjAge       -0.5205 0.5551  -1.6084   0.5674 0.3484
## AgeAccelGrim                      -0.0731 0.4530  -0.9609   0.8147 0.8718
## DNAmTLAdjAge                      -0.0404 0.0354  -0.1097   0.0290 0.2542
## IEAA                              -1.3120 0.5558  -2.4013  -0.2227 0.0182
## EEAA                               0.0801 0.6184  -1.1320   1.2922 0.8970
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                            <= 0.05
## EEAA                             > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2} + \epsilon\]

Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1034       0.1654
## Hannum EAA     0.0550       0.1306
## PhenoAge EAA   0.0653       0.1306
## Skin&Blood EAA 0.0353       0.1306
## GrimAge EAA    0.1928       0.2571
## DNAmTL         0.2487       0.2842
## IEAA           0.5142       0.5142
## EEAA           0.0263       0.1306

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0715       0.1834
## Hannum EAA     0.1314       0.2102
## PhenoAge EAA   0.2403       0.3204
## Skin&Blood EAA 0.0446       0.1834
## GrimAge EAA    0.0917       0.1834
## DNAmTL         0.4322       0.4322
## IEAA           0.3579       0.4090
## EEAA           0.0624       0.1834

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1992       0.3187
## Hannum EAA     0.1754       0.3187
## PhenoAge EAA   0.2713       0.3617
## Skin&Blood EAA 0.0873       0.2472
## GrimAge EAA    0.0319       0.2472
## DNAmTL         0.4242       0.4848
## IEAA           0.6646       0.6646
## EEAA           0.0927       0.2472

GEE (no confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $MEAS6_BC_PM_RET
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2510 0.6406  -1.5065   1.0046 0.6952
## AgeAccelerationResidualHannum     -0.3925 0.4998  -1.3721   0.5871 0.4323
## AgeAccelPheno                     -0.3475 0.6677  -1.6561   0.9611 0.6028
## DNAmAgeSkinBloodClockAdjAge        0.0746 0.4844  -0.8749   1.0241 0.8776
## AgeAccelGrim                       1.0200 0.6097  -0.1749   2.2149 0.0943
## DNAmTLAdjAge                       0.0175 0.0357  -0.0525   0.0875 0.6247
## IEAA                              -0.1676 0.5048  -1.1571   0.8218 0.7398
## EEAA                              -0.7563 0.6989  -2.1262   0.6136 0.2792
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_X31
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            1.2983 0.7075  -0.0883   2.6850 0.0665
## AgeAccelerationResidualHannum      0.9943 0.6643  -0.3077   2.2963 0.1344
## AgeAccelPheno                      1.0479 0.7016  -0.3273   2.4231 0.1353
## DNAmAgeSkinBloodClockAdjAge        1.3015 0.5981   0.1293   2.4738 0.0295
## AgeAccelGrim                       1.0821 0.3898   0.3182   1.8461 0.0055
## DNAmTLAdjAge                      -0.0229 0.0270  -0.0757   0.0300 0.3962
## IEAA                               0.6828 0.5403  -0.3761   1.7418 0.2063
## EEAA                               1.0135 0.7790  -0.5133   2.5403 0.1932
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                    <= 0.01
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_X5
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.1795 0.7126  -1.5762   1.2171 0.8011
## AgeAccelerationResidualHannum     -0.5288 0.6335  -1.7704   0.7129 0.4039
## AgeAccelPheno                     -0.1223 0.6350  -1.3669   1.1222 0.8472
## DNAmAgeSkinBloodClockAdjAge        0.6389 0.5676  -0.4737   1.7514 0.2604
## AgeAccelGrim                       0.5114 0.3940  -0.2609   1.2836 0.1943
## DNAmTLAdjAge                       0.0366 0.0253  -0.0130   0.0863 0.1481
## IEAA                               0.2756 0.5588  -0.8196   1.3708 0.6218
## EEAA                              -1.1792 0.7735  -2.6953   0.3370 0.1274
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_DlP
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.3500 0.7824  -1.1834   1.8835 0.6546
## AgeAccelerationResidualHannum      0.0773 0.6774  -1.2503   1.4049 0.9091
## AgeAccelPheno                      0.8949 0.6821  -0.4421   2.2319 0.1895
## DNAmAgeSkinBloodClockAdjAge       -1.0079 0.6161  -2.2153   0.1996 0.1018
## AgeAccelGrim                      -0.0330 0.6191  -1.2464   1.1803 0.9574
## DNAmTLAdjAge                      -0.0260 0.0316  -0.0880   0.0360 0.4108
## IEAA                               0.3756 0.6135  -0.8270   1.5781 0.5405
## EEAA                               0.0587 0.8749  -1.6562   1.7735 0.9465
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_NkF
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1813 0.7858  -1.3589   1.7216 0.8175
## AgeAccelerationResidualHannum      0.6335 0.6217  -0.5850   1.8519 0.3082
## AgeAccelPheno                     -0.2041 0.6978  -1.5718   1.1636 0.7699
## DNAmAgeSkinBloodClockAdjAge        0.2485 0.6376  -1.0012   1.4982 0.6967
## AgeAccelGrim                      -0.0278 0.4657  -0.9405   0.8850 0.9525
## DNAmTLAdjAge                      -0.0165 0.0281  -0.0715   0.0385 0.5558
## IEAA                              -0.3822 0.6852  -1.7253   0.9608 0.5770
## EEAA                               1.0090 0.8230  -0.6042   2.6222 0.2202
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $MEAS6_NO2_SO2
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2626 0.7499  -1.7325   1.2072 0.7262
## AgeAccelerationResidualHannum      0.5492 0.6170  -0.6601   1.7585 0.3734
## AgeAccelPheno                      0.8929 0.6070  -0.2968   2.0826 0.1413
## DNAmAgeSkinBloodClockAdjAge        0.1422 0.6079  -1.0494   1.3337 0.8151
## AgeAccelGrim                       0.3458 0.4086  -0.4552   1.1467 0.3975
## DNAmTLAdjAge                      -0.0429 0.0284  -0.0985   0.0127 0.1308
## IEAA                              -0.4182 0.6336  -1.6601   0.8237 0.5093
## EEAA                               0.3235 0.7842  -1.2135   1.8604 0.6800
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

3.5. Clusters based on urinary biomarkers (clusURI5)

The file “LEX_clusURI5.csv” has information on measured urinary biomarkers obtained during each visit. Estimates are available for 5 different prototypes (cluster variables) for a total of 163 subjects and 186 visits. The prototypes are labelled as:

URI5_NAP_1M_2M – a cluster of Naphthalene, 1Methylnaphthalene, and 2Methylnaphthalene
URI5_ACE – Acenaphthene only
URI5_FLU_PHE – Fluoranthene and Phenanthrene_anth
URI5_PYR – Pyrene only
URI5_CHR – Baa_Chrysene only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
MEAS6_BC_PM_RET 0.05 (-0.6, 0.5) -0.40 (-1.6, -0.3) 0.07 (-0.5, 0.5) 1.08 (0.5, 2.1)
(Missing) 70 10 57 3
MEAS6_X31 0.19 (-0.6, 0.7) -1.02 (-1.8, -0.8) 0.31 (-0.1, 0.8) 0.35 (-0.5, 0.8)
(Missing) 70 10 57 3
MEAS6_X5 -0.14 (-1.0, 1.0) -1.07 (-1.1, -1.0) 0.46 (-0.8, 1.1) 0.55 (-0.1, 0.9)
(Missing) 70 10 57 3
MEAS6_DlP -0.63 (-0.7, 1.3) 0.35 (-0.6, 1.0) -0.69 (-0.7, 1.2) -0.30 (-0.5, 1.3)
(Missing) 70 10 57 3
MEAS6_NkF -0.50 (-0.6, 1.2) -0.39 (-0.6, 0.6) -0.50 (-0.6, 1.2) -0.50 (-0.7, 0.2)
(Missing) 70 10 57 3
MEAS6_NO2_SO2 -0.08 (-0.9, 0.8) 0.98 (0.5, 1.5) -0.37 (-0.9, 0.8) -0.37 (-1.3, 0.2)
(Missing) 70 10 57 3
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7325       0.8371
## Hannum EAA     0.5927       0.8371
## PhenoAge EAA   0.0185       0.1480
## Skin&Blood EAA 0.4011       0.8022
## GrimAge EAA    0.0676       0.2704
## DNAmTL         0.1731       0.4616
## IEAA           0.8470       0.8470
## EEAA           0.6407       0.8371

GEE (Mix)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $URI5_NAP_1M_2M
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1520 0.5218  -0.8708   1.1748 0.7708
## AgeAccelerationResidualHannum     -0.4606 0.4434  -1.3296   0.4084 0.2989
## AgeAccelPheno                     -0.0180 0.4653  -0.9300   0.8940 0.9691
## DNAmAgeSkinBloodClockAdjAge       -0.2757 0.3827  -1.0259   0.4744 0.4712
## AgeAccelGrim                      -0.2704 0.4773  -1.2059   0.6652 0.5711
## DNAmTLAdjAge                      -0.0180 0.0351  -0.0868   0.0509 0.6090
## IEAA                               0.0067 0.5552  -1.0815   1.0949 0.9904
## EEAA                              -0.0176 0.5402  -1.0764   1.0411 0.9740
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_ACE
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0005 0.4278  -0.8390   0.8381 0.9991
## AgeAccelerationResidualHannum      0.4834 0.3333  -0.1699   1.1367 0.1470
## AgeAccelPheno                      0.8926 0.4735  -0.0355   1.8206 0.0594
## DNAmAgeSkinBloodClockAdjAge       -0.0704 0.3143  -0.6864   0.5456 0.8228
## AgeAccelGrim                      -1.2920 0.3333  -1.9453  -0.6387 0.0001
## DNAmTLAdjAge                       0.0026 0.0332  -0.0626   0.0677 0.9379
## IEAA                               0.3215 0.4291  -0.5196   1.1626 0.4537
## EEAA                               0.6255 0.4581  -0.2725   1.5234 0.1722
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                   <= 0.001
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_FLU_PHE
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.3410 0.4843  -0.6081   1.2902 0.4813
## AgeAccelerationResidualHannum      0.1956 0.4495  -0.6853   1.0766 0.6634
## AgeAccelPheno                      0.4084 0.4202  -0.4152   1.2320 0.3311
## DNAmAgeSkinBloodClockAdjAge        0.0197 0.3263  -0.6199   0.6593 0.9519
## AgeAccelGrim                       1.0882 1.6256  -2.0980   4.2743 0.5032
## DNAmTLAdjAge                      -0.0235 0.0691  -0.1588   0.1119 0.7340
## IEAA                               0.2347 0.4903  -0.7263   1.1956 0.6322
## EEAA                               0.5243 0.5941  -0.6402   1.6888 0.3775
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_PYR
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0587 0.4785  -0.8790   0.9965 0.9023
## AgeAccelerationResidualHannum      0.3959 0.4305  -0.4478   1.2396 0.3577
## AgeAccelPheno                      1.1174 0.4847   0.1674   2.0673 0.0211
## DNAmAgeSkinBloodClockAdjAge        0.6638 0.4167  -0.1528   1.4805 0.1111
## AgeAccelGrim                      -0.1384 1.5144  -3.1065   2.8298 0.9272
## DNAmTLAdjAge                      -0.0365 0.0323  -0.0997   0.0267 0.2577
## IEAA                              -0.0151 0.4872  -0.9700   0.9398 0.9753
## EEAA                               0.6232 0.5492  -0.4533   1.6997 0.2565
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_CHR
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1114 0.3838  -0.6408   0.8637 0.7716
## AgeAccelerationResidualHannum      0.0802 0.4224  -0.7476   0.9080 0.8494
## AgeAccelPheno                      0.0207 0.3799  -0.7239   0.7653 0.9566
## DNAmAgeSkinBloodClockAdjAge        0.0762 0.3130  -0.5373   0.6897 0.8077
## AgeAccelGrim                       0.1398 0.7358  -1.3023   1.5820 0.8493
## DNAmTLAdjAge                       0.0178 0.3096  -0.5891   0.6247 0.9542
## IEAA                              -0.1849 0.4274  -1.0225   0.6528 0.6653
## EEAA                               0.2934 0.5105  -0.7071   1.2939 0.5654
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

GEE (Mix, mutual adjust)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * NAP\_1M\_2M + \beta_2 ACE + \beta_3 FLU\_PHE + \beta_4 PYR + \beta_5 CHR \\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_9 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## The estimated effects:
## $URI5_NAP_1M_2M
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0772 0.6297  -1.3115   1.1571 0.9024
## AgeAccelerationResidualHannum     -0.9973 0.5285  -2.0332   0.0385 0.0591
## AgeAccelPheno                     -0.4612 0.6006  -1.6384   0.7161 0.4426
## DNAmAgeSkinBloodClockAdjAge       -0.4410 0.5162  -1.4528   0.5707 0.3929
## AgeAccelGrim                       0.0385 1.3493  -2.6061   2.6831 0.9773
## DNAmTLAdjAge                      -0.0117 0.1841  -0.3726   0.3492 0.9492
## IEAA                              -0.3117 0.6536  -1.5928   0.9694 0.6334
## EEAA                              -0.6005 0.6512  -1.8768   0.6758 0.3564
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_ACE
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0026 0.4246  -0.8348   0.8297 0.9952
## AgeAccelerationResidualHannum      0.5312 0.3182  -0.0925   1.1548 0.0950
## AgeAccelPheno                      0.9146 0.4577   0.0175   1.8118 0.0457
## DNAmAgeSkinBloodClockAdjAge       -0.0633 0.2924  -0.6364   0.5097 0.8285
## AgeAccelGrim                       0.3306 3.8727  -7.2599   7.9212 0.9320
## DNAmTLAdjAge                      -0.0277 0.0504  -0.1265   0.0712 0.5833
## IEAA                               0.2828 0.4335  -0.5668   1.1325 0.5141
## EEAA                               0.6471 0.4666  -0.2675   1.5616 0.1655
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_FLU_PHE
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.4942 0.7136  -0.9044   1.8928 0.4886
## AgeAccelerationResidualHannum      0.8156 0.5959  -0.3524   1.9835 0.1711
## AgeAccelPheno                      0.0888 0.6202  -1.1268   1.3044 0.8862
## DNAmAgeSkinBloodClockAdjAge       -0.1798 0.4682  -1.0975   0.7379 0.7009
## AgeAccelGrim                       0.3924 3.4441  -6.3580   7.1428 0.9093
## DNAmTLAdjAge                      -0.0361 0.2106  -0.4488   0.3767 0.8639
## IEAA                               0.7557 0.6585  -0.5350   2.0464 0.2512
## EEAA                               0.6230 0.7469  -0.8408   2.0869 0.4042
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_PYR
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.2034 0.5618  -1.3044   0.8977 0.7174
## AgeAccelerationResidualHannum      0.2590 0.3895  -0.5044   1.0223 0.5061
## AgeAccelPheno                      1.2211 0.5215   0.1989   2.2432 0.0192
## DNAmAgeSkinBloodClockAdjAge        0.9278 0.4547   0.0366   1.8189 0.0413
## AgeAccelGrim                       0.2333 1.2996  -2.3139   2.7805 0.8575
## DNAmTLAdjAge                       0.0256 0.0751  -0.1216   0.1728 0.7331
## IEAA                              -0.2820 0.6001  -1.4582   0.8941 0.6384
## EEAA                               0.4017 0.5142  -0.6061   1.4096 0.4346
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge     <= 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_CHR
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.0725 0.4479  -0.9504   0.8054 0.8714
## AgeAccelerationResidualHannum      0.0483 0.4376  -0.8094   0.9061 0.9120
## AgeAccelPheno                     -0.0112 0.3626  -0.7219   0.6996 0.9754
## DNAmAgeSkinBloodClockAdjAge        0.1188 0.3586  -0.5841   0.8217 0.7405
## AgeAccelGrim                       0.2797 0.4074  -0.5189   1.0783 0.4924
## DNAmTLAdjAge                       0.0028 0.0273  -0.0507   0.0564 0.9170
## IEAA                              -0.3520 0.4199  -1.1751   0.4711 0.4019
## EEAA                               0.1725 0.5044  -0.8162   1.1612 0.7323
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

Sensitivity analyses

Likelihood ratio (LR) test (no confounders)

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7166       0.9146
## Hannum EAA     0.8605       0.9146
## PhenoAge EAA   0.0779       0.5412
## Skin&Blood EAA 0.5460       0.9146
## GrimAge EAA    0.2178       0.5808
## DNAmTL         0.1353       0.5412
## IEAA           0.7881       0.9146
## EEAA           0.9146       0.9146

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5630       0.9535
## Hannum EAA     0.7871       0.9535
## PhenoAge EAA   0.1480       0.9535
## Skin&Blood EAA 0.9124       0.9535
## GrimAge EAA    0.8240       0.9535
## DNAmTL         0.5162       0.9535
## IEAA           0.7267       0.9535
## EEAA           0.9535       0.9535

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7266       0.9357
## Hannum EAA     0.8797       0.9357
## PhenoAge EAA   0.1130       0.9040
## Skin&Blood EAA 0.6715       0.9357
## GrimAge EAA    0.8736       0.9357
## DNAmTL         0.6495       0.9357
## IEAA           0.8091       0.9357
## EEAA           0.9357       0.9357

GEE (no confounders)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effects:
## $URI5_NAP_1M_2M
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.1451 0.4798  -0.7953   1.0855 0.7624
## AgeAccelerationResidualHannum     -0.4140 0.4297  -1.2562   0.4282 0.3353
## AgeAccelPheno                     -0.0402 0.4520  -0.9261   0.8457 0.9292
## DNAmAgeSkinBloodClockAdjAge       -0.2916 0.3794  -1.0352   0.4521 0.4422
## AgeAccelGrim                       0.7905 0.7292  -0.6387   2.2197 0.2783
## DNAmTLAdjAge                      -0.0211 0.0165  -0.0535   0.0112 0.2007
## IEAA                              -0.0220 0.5284  -1.0576   1.0136 0.9668
## EEAA                              -0.0926 0.5295  -1.1303   0.9452 0.8612
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_ACE
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.1857 0.4469  -1.0617   0.6902 0.6777
## AgeAccelerationResidualHannum      0.4262 0.3152  -0.1917   1.0440 0.1764
## AgeAccelPheno                      0.7347 0.4369  -0.1215   1.5909 0.0926
## DNAmAgeSkinBloodClockAdjAge       -0.0918 0.3105  -0.7003   0.5168 0.7676
## AgeAccelGrim                       0.9474 0.7988  -0.6183   2.5131 0.2356
## DNAmTLAdjAge                      -0.0106 0.0124  -0.0349   0.0137 0.3914
## IEAA                               0.1276 0.4252  -0.7057   0.9609 0.7641
## EEAA                               0.5001 0.4234  -0.3297   1.3299 0.2375
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_FLU_PHE
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0150 0.4508  -0.8685   0.8985 0.9734
## AgeAccelerationResidualHannum      0.2083 0.4207  -0.6164   1.0329 0.6206
## AgeAccelPheno                      0.4695 0.4311  -0.3756   1.3145 0.2762
## DNAmAgeSkinBloodClockAdjAge       -0.0025 0.3205  -0.6306   0.6257 0.9939
## AgeAccelGrim                       0.7458 0.5193  -0.2720   1.7636 0.1509
## DNAmTLAdjAge                      -0.0383 0.0160  -0.0698  -0.0069 0.0168
## IEAA                               0.1830 0.4829  -0.7636   1.1295 0.7048
## EEAA                               0.4333 0.5655  -0.6750   1.5416 0.4435
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                    <= 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_PYR
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual           -0.4791 0.4380  -1.3376   0.3794 0.2740
## AgeAccelerationResidualHannum      0.3009 0.3733  -0.4308   1.0325 0.4202
## AgeAccelPheno                      1.0068 0.4239   0.1759   1.8378 0.0176
## DNAmAgeSkinBloodClockAdjAge        0.5425 0.3945  -0.2307   1.3157 0.1691
## AgeAccelGrim                       0.4524 0.3209  -0.1765   1.0813 0.1585
## DNAmTLAdjAge                      -0.0245 0.0185  -0.0608   0.0117 0.1840
## IEAA                              -0.2856 0.4530  -1.1734   0.6023 0.5284
## EEAA                               0.3395 0.4839  -0.6090   1.2879 0.4830
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                   <= 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05
## 
## $URI5_CHR
##                               coefficient    std ci_lower ci_upper  p_val
## AgeAccelerationResidual            0.0122 0.3673  -0.7077   0.7321 0.9734
## AgeAccelerationResidualHannum      0.0647 0.3947  -0.7089   0.8383 0.8699
## AgeAccelPheno                      0.0456 0.3893  -0.7173   0.8086 0.9067
## DNAmAgeSkinBloodClockAdjAge        0.0382 0.2859  -0.5222   0.5986 0.8938
## AgeAccelGrim                       0.4298 0.2937  -0.1458   1.0054 0.1433
## DNAmTLAdjAge                      -0.0272 0.0223  -0.0708   0.0165 0.2226
## IEAA                              -0.1711 0.3912  -0.9377   0.5956 0.6619
## EEAA                               0.1957 0.4975  -0.7795   1.1709 0.6941
##                               sig_level
## AgeAccelerationResidual          > 0.05
## AgeAccelerationResidualHannum    > 0.05
## AgeAccelPheno                    > 0.05
## DNAmAgeSkinBloodClockAdjAge      > 0.05
## AgeAccelGrim                     > 0.05
## DNAmTLAdjAge                     > 0.05
## IEAA                             > 0.05
## EEAA                             > 0.05

4.1. Current exposure to 5MC

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
cur_5mc 7.81 (5.2, 9.7) 2.59 (2.2, 4.0) 9.46 (5.6, 10.1) 7.40 (7.1, 7.4)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{cur_5mc} \\ & + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses + \beta_{5} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8392       0.9109
## Hannum EAA     0.6376       0.9109
## PhenoAge EAA   0.2551       0.6803
## Skin&Blood EAA 0.1507       0.6028
## GrimAge EAA    0.0013       0.0104
## DNAmTL         0.4367       0.8734
## IEAA           0.9109       0.9109
## EEAA           0.6856       0.9109

GEE (mix)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cur\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## [1] " Result data: "
##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.015878690   0.123212614165765 -0.22561803
## AgeAccelerationResidualHannum -0.051171412   0.103935109316447 -0.25488423
## AgeAccelPheno                  0.091359722  0.0974953712583224 -0.09973121
## DNAmAgeSkinBloodClockAdjAge    0.114010349  0.0766727133240309 -0.03626817
## AgeAccelGrim                   0.167727453  0.0513461606401706  0.06708898
## DNAmTLAdjAge                  -0.004550865 0.00347496993471429 -0.01136181
## IEAA                           0.003400224    0.13227439652008 -0.25585759
## EEAA                          -0.068201795    0.13857957225156 -0.33981776
##                                  ci_upper               p_val sig_level
## AgeAccelerationResidual       0.257375413   0.897458716697316    > 0.05
## AgeAccelerationResidualHannum 0.152541402   0.622479003468804    > 0.05
## AgeAccelPheno                 0.282450650   0.348723953264527    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.264288867   0.137021616586443    > 0.05
## AgeAccelGrim                  0.268365928 0.00108846725724532   <= 0.01
## DNAmTLAdjAge                  0.002260076   0.190326866190183    > 0.05
## IEAA                          0.262658041   0.979491967938557    > 0.05
## EEAA                          0.203414167   0.622614029655613    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Sensitivity analysis

GEE (no confounders)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cur\_5mc + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## [1] " Result data: "
##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.019159838   0.119620299681397 -0.21529595
## AgeAccelerationResidualHannum -0.041572743    0.10121495306854 -0.23995405
## AgeAccelPheno                  0.063948683   0.100238231551026 -0.13251825
## DNAmAgeSkinBloodClockAdjAge    0.104833952   0.075228712841312 -0.04261433
## AgeAccelGrim                   0.149523037  0.0565650873465267  0.03865547
## DNAmTLAdjAge                  -0.003814336 0.00353079663956072 -0.01073470
## IEAA                           0.001582042   0.127834093339104 -0.24897278
## EEAA                          -0.053365322   0.132090247562408 -0.31226221
##                                  ci_upper               p_val sig_level
## AgeAccelerationResidual       0.253615625   0.872745484941928    > 0.05
## AgeAccelerationResidualHannum 0.156808565   0.681265269321088    > 0.05
## AgeAccelPheno                 0.260415616   0.523495170158012    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.252282229   0.163457636388872    > 0.05
## AgeAccelGrim                  0.260390608 0.00820827841291805   <= 0.01
## DNAmTLAdjAge                  0.003106025   0.280006515297229    > 0.05
## IEAA                          0.252136865   0.990125837758072    > 0.05
## EEAA                          0.205531563   0.686207920603063    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Likelihood ratio (LR) test (no confounders)

Full model: \[ Y = \beta_0 + \beta_1 * cur\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8549       0.9308
## Hannum EAA     0.6051       0.8797
## PhenoAge EAA   0.4505       0.8797
## Skin&Blood EAA 0.2021       0.8084
## GrimAge EAA    0.0079       0.0632
## DNAmTL         0.5893       0.8797
## IEAA           0.9308       0.9308
## EEAA           0.6598       0.8797

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[ Y = \beta_0 + \beta_1 * cur\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8991       0.9505
## Hannum EAA     0.5246       0.7767
## PhenoAge EAA   0.4812       0.7767
## Skin&Blood EAA 0.2037       0.7767
## GrimAge EAA    0.0034       0.0272
## DNAmTL         0.5825       0.7767
## IEAA           0.9505       0.9505
## EEAA           0.5364       0.7767

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[ Y = \beta_0 + \beta_1 * cur\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4786       0.7658
## Hannum EAA     0.9613       0.9613
## PhenoAge EAA   0.3232       0.6560
## Skin&Blood EAA 0.2537       0.6560
## GrimAge EAA    0.0085       0.0680
## DNAmTL         0.6601       0.8801
## IEAA           0.3280       0.6560
## EEAA           0.7723       0.8826

4.2. Cumulative exposure to 5MC

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
cum_5mc 253.00 (157.7, 371.9) 92.43 (82.6, 167.9) 270.50 (179.9, 389.1) 341.46 (228.8, 471.5)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{cum_5mc} \\ & + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses + \beta_{5} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7436       0.7436
## Hannum EAA     0.1673       0.3346
## PhenoAge EAA   0.0945       0.2520
## Skin&Blood EAA 0.2460       0.3936
## GrimAge EAA    0.0004       0.0032
## DNAmTL         0.4779       0.6372
## IEAA           0.7222       0.7436
## EEAA           0.0927       0.2520

GEE (mix)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cum\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## [1] " Result data: "
##                                 coefficient                  std      ci_lower
## AgeAccelerationResidual        0.0002634684  0.00356364056862806 -0.0067212672
## AgeAccelerationResidualHannum  0.0034941270  0.00303739789484009 -0.0024591729
## AgeAccelPheno                  0.0044586214  0.00306589829668175 -0.0015505393
## DNAmAgeSkinBloodClockAdjAge    0.0025381617  0.00268283943639575 -0.0027202036
## AgeAccelGrim                   0.0059797751  0.00174133579802403  0.0025667569
## DNAmTLAdjAge                  -0.0001321556 0.000141285261175084 -0.0004090747
## IEAA                          -0.0019359695     0.00335384774017 -0.0085095111
## EEAA                           0.0054840443  0.00389919260451722 -0.0021583732
##                                   ci_upper                p_val sig_level
## AgeAccelerationResidual       0.0072482039    0.941064206728588    > 0.05
## AgeAccelerationResidualHannum 0.0094474269    0.249992106478145    > 0.05
## AgeAccelPheno                 0.0104677820    0.145873502832328    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.0077965270    0.344111409021191    > 0.05
## AgeAccelGrim                  0.0093927932 0.000594708988064241  <= 0.001
## DNAmTLAdjAge                  0.0001447635    0.349591731827019    > 0.05
## IEAA                          0.0046375721    0.563778458819213    > 0.05
## EEAA                          0.0131264618    0.159588653069498    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Sensitivity analysis

GEE (no confounders)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cum\_5mc + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## [1] " Result data: "
##                                 coefficient                  std      ci_lower
## AgeAccelerationResidual       -0.0015980048  0.00286017417861232 -0.0072039462
## AgeAccelerationResidualHannum  0.0028430093  0.00255680408795988 -0.0021683267
## AgeAccelPheno                  0.0037546145  0.00263382056095098 -0.0014076738
## DNAmAgeSkinBloodClockAdjAge    0.0016390285  0.00216266982751781 -0.0025998044
## AgeAccelGrim                   0.0039938498  0.00164896030877211  0.0007618876
## DNAmTLAdjAge                  -0.0001014871 0.000111871519540741 -0.0003207553
## IEAA                          -0.0021285213  0.00269473488082702 -0.0074102016
## EEAA                           0.0037313699  0.00327771758612151 -0.0026929566
##                                   ci_upper              p_val sig_level
## AgeAccelerationResidual       0.0040079366  0.576360404235931    > 0.05
## AgeAccelerationResidualHannum 0.0078543454  0.266164520047206    > 0.05
## AgeAccelPheno                 0.0089169028  0.154001391118467    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.0058778613  0.448527185647545    > 0.05
## AgeAccelGrim                  0.0072258120 0.0154335997193696   <= 0.05
## DNAmTLAdjAge                  0.0001177811   0.36431393900575    > 0.05
## IEAA                          0.0031531591   0.42959697033491    > 0.05
## EEAA                          0.0101556963   0.25495143497931    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Likelihood ratio (LR) test (no confounders)

Full model: \[ Y = \beta_0 + \beta_1 * cum\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6262       0.6842
## Hannum EAA     0.3685       0.6842
## PhenoAge EAA   0.1953       0.6842
## Skin&Blood EAA 0.5237       0.6842
## GrimAge EAA    0.0350       0.2800
## DNAmTL         0.6842       0.6842
## IEAA           0.4877       0.6842
## EEAA           0.3180       0.6842

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[ Y = \beta_0 + \beta_1 * cum\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7033       0.7033
## Hannum EAA     0.3100       0.4960
## PhenoAge EAA   0.0621       0.2484
## Skin&Blood EAA 0.1114       0.2971
## GrimAge EAA    0.0300       0.2400
## DNAmTL         0.5423       0.6198
## IEAA           0.5021       0.6198
## EEAA           0.2754       0.4960

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[ Y = \beta_0 + \beta_1 * cum\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9653       0.9946
## Hannum EAA     0.1054       0.2502
## PhenoAge EAA   0.0318       0.1792
## Skin&Blood EAA 0.1487       0.2502
## GrimAge EAA    0.0448       0.1792
## DNAmTL         0.6183       0.8244
## IEAA           0.9946       0.9946
## EEAA           0.1564       0.2502

4.3. Childhood exposure to 5MC

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
bir_5mc 4.83 (2.6, 8.2) 2.10 (1.5, 4.4) 4.83 (3.0, 8.2) 8.02 (3.7, 8.8)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{childhood_5mc} \\ & + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses + \beta_{5} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9511       0.9511
## Hannum EAA     0.1340       0.2144
## PhenoAge EAA   0.0176       0.0704
## Skin&Blood EAA 0.0984       0.1968
## GrimAge EAA    0.0004       0.0032
## DNAmTL         0.8526       0.9511
## IEAA           0.3279       0.4372
## EEAA           0.0790       0.1968

GEE (mix)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *childhood\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## [1] " Result data: "
##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.002549647   0.175648408840649 -0.34172123
## AgeAccelerationResidualHannum  0.214124879   0.156901216094478 -0.09340150
## AgeAccelPheno                  0.328090148    0.15503211923717  0.02422719
## DNAmAgeSkinBloodClockAdjAge    0.184739910   0.127394623047048 -0.06495355
## AgeAccelGrim                   0.316489482  0.0963285908716984  0.12768544
## DNAmTLAdjAge                  -0.003382397 0.00704699006180516 -0.01719450
## IEAA                          -0.172753745   0.164532364342251 -0.49523718
## EEAA                           0.313411448   0.201414432212561 -0.08136084
##                                ci_upper               p_val sig_level
## AgeAccelerationResidual       0.3468205   0.988418609834732    > 0.05
## AgeAccelerationResidualHannum 0.5216513   0.172343774292717    > 0.05
## AgeAccelPheno                 0.6319531  0.0343216724056407   <= 0.05
## DNAmAgeSkinBloodClockAdjAge   0.4344334   0.147019764983751    > 0.05
## AgeAccelGrim                  0.5052935 0.00101794428051305   <= 0.01
## DNAmTLAdjAge                  0.0104297   0.631243324757604    > 0.05
## IEAA                          0.1497297   0.293732747964983    > 0.05
## EEAA                          0.7081837   0.119695588251105    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Sensitivity analysis

GEE (no confounders)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *childhood\_5mc + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

Results:

## [1] " Result data: "
##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.019159838   0.119620299681397 -0.21529595
## AgeAccelerationResidualHannum -0.041572743    0.10121495306854 -0.23995405
## AgeAccelPheno                  0.063948683   0.100238231551026 -0.13251825
## DNAmAgeSkinBloodClockAdjAge    0.104833952   0.075228712841312 -0.04261433
## AgeAccelGrim                   0.149523037  0.0565650873465267  0.03865547
## DNAmTLAdjAge                  -0.003814336 0.00353079663956072 -0.01073470
## IEAA                           0.001582042   0.127834093339104 -0.24897278
## EEAA                          -0.053365322   0.132090247562408 -0.31226221
##                                  ci_upper               p_val sig_level
## AgeAccelerationResidual       0.253615625   0.872745484941928    > 0.05
## AgeAccelerationResidualHannum 0.156808565   0.681265269321088    > 0.05
## AgeAccelPheno                 0.260415616   0.523495170158012    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.252282229   0.163457636388872    > 0.05
## AgeAccelGrim                  0.260390608 0.00820827841291805   <= 0.01
## DNAmTLAdjAge                  0.003106025   0.280006515297229    > 0.05
## IEAA                          0.252136865   0.990125837758072    > 0.05
## EEAA                          0.205531563   0.686207920603063    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Likelihood ratio (LR) test (no confounders)

Full model: \[ Y = \beta_0 + \beta_1 * childhood\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8121       0.8478
## Hannum EAA     0.2726       0.4212
## PhenoAge EAA   0.0454       0.1816
## Skin&Blood EAA 0.1684       0.4060
## GrimAge EAA    0.0052       0.0416
## DNAmTL         0.8478       0.8478
## IEAA           0.3159       0.4212
## EEAA           0.2030       0.4060

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[ Y = \beta_0 + \beta_1 * childhood\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5762       0.6585
## Hannum EAA     0.2975       0.3967
## PhenoAge EAA   0.0478       0.1803
## Skin&Blood EAA 0.0676       0.1803
## GrimAge EAA    0.0070       0.0560
## DNAmTL         0.8067       0.8067
## IEAA           0.1818       0.3636
## EEAA           0.2303       0.3685

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[ Y = \beta_0 + \beta_1 * childhood\_5mc + \epsilon \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7809       0.7809
## Hannum EAA     0.1261       0.2466
## PhenoAge EAA   0.0323       0.1292
## Skin&Blood EAA 0.1005       0.2466
## GrimAge EAA    0.0086       0.0688
## DNAmTL         0.6247       0.7139
## IEAA           0.4805       0.6407
## EEAA           0.1541       0.2466

5. Ambient Exposure

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
bap_air 39.44 (18.9, 74.1) 10.09 (4.5, 20.7) 45.22 (21.9, 76.7) 69.11 (57.0, 131.2)
(Missing) 4 0 3 1
pm25_air 139.32 (100.1, 227.1) 120.16 (102.4, 160.7) 137.48 (98.3, 211.0) 421.89 (252.7, 480.4)
ANY_air 564.51 (305.8, 977.5) 477.86 (187.2, 791.4) 560.77 (306.0, 914.7) 7,030.90 (3,125.6, 10,967.7)
(Missing) 35 7 24 4
BPE_air 46.55 (19.5, 73.4) 12.70 (3.9, 19.7) 48.29 (22.9, 83.4) 66.81 (42.4, 114.8)
(Missing) 4 0 3 1
BaA_air 40.51 (16.7, 88.1) 9.44 (2.9, 23.3) 50.23 (20.7, 106.2) 68.31 (61.8, 163.2)
(Missing) 4 0 3 1
BbF_air 62.69 (32.8, 120.9) 31.76 (13.5, 50.1) 65.78 (34.5, 124.7) 88.69 (78.2, 181.6)
(Missing) 4 0 3 1
BkF_air 13.24 (6.4, 25.9) 3.37 (2.0, 7.6) 15.07 (8.0, 28.6) 27.64 (12.5, 48.0)
(Missing) 4 0 3 1
CHR_air 45.82 (16.4, 86.9) 15.24 (4.9, 31.8) 50.79 (18.1, 86.9) 91.89 (61.3, 134.8)
(Missing) 4 0 3 1
DBA_air 12.49 (4.4, 27.5) 3.92 (1.4, 11.0) 14.25 (6.1, 31.8) 12.67 (7.6, 25.3)
(Missing) 4 0 3 1
FLT_air 17.33 (5.1, 41.6) 4.35 (0.6, 7.2) 19.15 (6.5, 41.8) 104.71 (48.9, 175.2)
(Missing) 4 0 3 1
FLU_air 276.10 (165.2, 546.9) 251.42 (219.0, 298.2) 276.10 (159.0, 544.6) 1,426.05 (632.8, 2,241.9)
(Missing) 35 7 24 4
IPY_air 27.29 (14.0, 47.7) 12.70 (4.3, 16.6) 30.70 (15.3, 48.1) 69.17 (51.1, 118.8)
(Missing) 4 0 3 1
NAP_air 3,170.67 (1,807.5, 5,568.9) 3,217.69 (2,288.3, 4,623.5) 3,142.04 (1,759.1, 5,442.8) 29,828.64 (11,068.1, 49,775.1)
(Missing) 35 7 24 4
PHE_air 396.14 (220.9, 820.9) 363.30 (294.3, 550.4) 380.03 (206.2, 771.8) 2,120.65 (907.6, 3,404.2)
(Missing) 35 7 24 4
PYR_air 21.81 (6.1, 51.3) 6.42 (0.6, 8.2) 23.96 (7.7, 51.3) 108.99 (71.5, 191.4)
(Missing) 4 0 3 1
1 Median (IQR)

Primary analysis

GEE for each ambient exposure measurement (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *X\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the ambient exposure measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \beta_{16} * county + \beta_{17} * BMI + \beta_{18} * ses + \beta_{19} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0119       0.0476
## Hannum EAA     0.2902       0.3317
## PhenoAge EAA   0.0990       0.1980
## Skin&Blood EAA 0.1515       0.2424
## GrimAge EAA    0.0018       0.0144
## DNAmTL         0.4917       0.4917
## IEAA           0.0335       0.0893
## EEAA           0.2244       0.2992

Sensitivity analysis

GEE for each ambient exposure measurement (no confounders)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *X + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the ambient exposure measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

Likelihood ratio (LR) test (no confounders)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \epsilon \end{aligned} \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0124       0.0440
## Hannum EAA     0.2385       0.3023
## PhenoAge EAA   0.0864       0.1728
## Skin&Blood EAA 0.2439       0.3023
## GrimAge EAA    0.0165       0.0440
## DNAmTL         0.5855       0.5855
## IEAA           0.0154       0.0440
## EEAA           0.2645       0.3023

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \epsilon \end{aligned} \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4481       0.9041
## Hannum EAA     0.6843       0.9041
## PhenoAge EAA   0.6391       0.9041
## Skin&Blood EAA 0.9041       0.9041
## GrimAge EAA    0.1945       0.9041
## DNAmTL         0.8384       0.9041
## IEAA           0.2352       0.9041
## EEAA           0.6906       0.9041

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \epsilon \end{aligned} \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7795       0.9656
## Hannum EAA     0.8191       0.9656
## PhenoAge EAA   0.4285       0.9656
## Skin&Blood EAA 0.8324       0.9656
## GrimAge EAA    0.0552       0.4416
## DNAmTL         0.9656       0.9656
## IEAA           0.3845       0.9656
## EEAA           0.8923       0.9656

6. Urinary Measurements

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
Benzanthracene_Chrysene_urine 0.38 (0.3, 0.8) 0.29 (0.1, 0.6) 0.45 (0.3, 1.0) 0.36 (0.3, 0.6)
(Missing) 2 0 2 0
Naphthalene_urine 107.58 (72.1, 168.8) 96.94 (54.9, 110.9) 108.85 (73.5, 169.3) 141.97 (99.7, 174.6)
Methylnaphthalene_2_urine 26.67 (17.9, 45.0) 17.92 (8.8, 23.4) 30.18 (20.9, 46.4) 20.30 (12.2, 34.2)
(Missing) 7 0 7 0
Methylnaphthalene_1_urine 10.93 (6.6, 18.1) 5.26 (3.6, 10.5) 11.52 (7.7, 20.9) 15.06 (11.0, 26.7)
(Missing) 4 1 3 0
Acenaphthene_urine 3.14 (2.2, 7.3) 2.82 (2.2, 3.5) 3.38 (2.3, 7.9) 3.58 (2.0, 7.2)
Phenanthrene_Anthracene_urine 112.78 (42.4, 239.6) 78.75 (41.6, 135.5) 115.58 (56.8, 239.7) 109.86 (39.6, 305.8)
Fluoranthene_urine 16.53 (6.1, 23.1) 17.68 (5.4, 20.8) 15.25 (6.3, 23.2) 23.23 (22.4, 36.0)
Pyrene_urine 0.54 (0.4, 0.8) 0.41 (0.4, 0.4) 0.54 (0.4, 0.8) 0.78 (0.7, 0.9)
(Missing) 15 7 7 1
1 Median (IQR)

Primary analysis

GEE for each ambient exposure measurement (mix)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *X \\ & + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses + \beta_{5} * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the urinary exposure measurements.

Results:

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \beta_{10} * county + \beta_{11} * BMI + \beta_{12} * ses + \beta_{13} * edu + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \epsilon \end{aligned} \]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0006       0.0012
## Hannum EAA     0.0221       0.0295
## PhenoAge EAA   0.0001       0.0008
## Skin&Blood EAA 0.0011       0.0018
## GrimAge EAA    0.0003       0.0008
## DNAmTL         0.0312       0.0357
## IEAA           0.0002       0.0008
## EEAA           0.0504       0.0504

Sensitivity analysis

GEE for each ambient exposure measurement (no confounders)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *X + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the urinary exposure measurements.

Results:

Likelihood ratio (LR) test (no confounders)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0012       0.0048
## Hannum EAA     0.0860       0.0983
## PhenoAge EAA   0.0009       0.0048
## Skin&Blood EAA 0.0075       0.0150
## GrimAge EAA    0.0145       0.0232
## DNAmTL         0.0342       0.0456
## IEAA           0.0029       0.0077
## EEAA           0.1986       0.1986

Likelihood ratio (LR) test (no confounders) with subjects using only smoky or smokeless coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0318       0.0636
## Hannum EAA     0.1305       0.1959
## PhenoAge EAA   0.0049       0.0196
## Skin&Blood EAA 0.0009       0.0072
## GrimAge EAA    0.4144       0.4144
## DNAmTL         0.1663       0.1959
## IEAA           0.0170       0.0453
## EEAA           0.1714       0.1959

Likelihood ratio (LR) test (no confounders) with subjects only using smoky coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3420       0.4325
## Hannum EAA     0.3784       0.4325
## PhenoAge EAA   0.0197       0.1076
## Skin&Blood EAA 0.1371       0.3656
## GrimAge EAA    0.4957       0.4957
## DNAmTL         0.2194       0.4325
## IEAA           0.0269       0.1076
## EEAA           0.3324       0.4325